The network characteristic of the central neural system has been widely accepted as a basic fabric form. However,the matrix characteristics of neural network are still not fully understood. If we ignore the matrix cha...The network characteristic of the central neural system has been widely accepted as a basic fabric form. However,the matrix characteristics of neural network are still not fully understood. If we ignore the matrix characteristics of the neural networks and just pay close attention to its connection mode,we are likely to fall into the theory of mechanical reductionism. This can lead to a problem in representing consciousness in a disadvantageous situation. It can also be a barrier to further improving the global workspace theory. Incomplete elucidation of the mechanisms of consciousness representation can also affect the assessment of the surgical outcome of partial epilepsy with conscious injury. Therefore,this paper reviews the epistemological development of neuroscience. We will initially describe the matrix characteristics of the neural system and their significance to the information processing mechanism,and further explore the role of neural matrix in identifying cases of partial epilepsy with little effect on the resection of the lesion.展开更多
The matrix metalloproteinases（MMPs） are a family of zinc-dependent endopeptidases originally characterized as secreted proteases responsible for degrading extracellular matrix proteins.Their canonical role in matrix...The matrix metalloproteinases（MMPs） are a family of zinc-dependent endopeptidases originally characterized as secreted proteases responsible for degrading extracellular matrix proteins.Their canonical role in matrix remodelling is of significant importance in neural development and regeneration,but emerging roles for MMPs,especially in signal transduction pathways,are also of obvious importance in a neural context.Misregulation of MMP activity is a hallmark of many neuropathologies,and members of every branch of the MMP family have been implicated in aspects of neural development and disease.However,while extraordinary research efforts have been made to elucidate the molecular mechanisms involving MMPs,methodological constraints and complexities of the research models have impeded progress.Here we discuss the current state of our understanding of the roles of MMPs in neural development using recent examples and advocate a phylogenetically diverse approach to MMP research as a means to both circumvent the challenges associated with specific model organisms,and to provide a broader evolutionary context from which to synthesize an understanding of the underlying biology.展开更多
Gate matrix layout problem plays an important role in integrated circuit design, but its optimization is NP-hard. In this paper, typical gate layout problem is analysed and adapted to neural network representation, fu...Gate matrix layout problem plays an important role in integrated circuit design, but its optimization is NP-hard. In this paper, typical gate layout problem is analysed and adapted to neural network representation, furthermore the simulated results are given.展开更多
In this paper we discuss neural network-based matrix effect correction in energy dispersive X-ray fluorescence (EDXRF) analysis,with detailed algorithm to classify the samples.The method can correct the matrix effect ...In this paper we discuss neural network-based matrix effect correction in energy dispersive X-ray fluorescence (EDXRF) analysis,with detailed algorithm to classify the samples.The method can correct the matrix effect effectively through classifying the samples automatically,and influence of X-ray absorption and enhancement by major elements of the samples is reduced.Experiments for the complex matrix effect correction in EDXRF analysis of samples in Pangang showed improved accuracy of the elemental analysis result.展开更多
In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented. This class of chaotic neural networks covers several well-known neural networks, such as Hopfield n...In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented. This class of chaotic neural networks covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks. The obtained criteria are expressed in terms of linear matrix inequalities, thus they can be efficiently verified. A comparison between our results and the previous results shows that our results are less restrictive.展开更多
Cognitive decline is a feature of normal and pathological aging. As the proportion of the global aged population continues to grow, it is imperative to understand the molecular and cellular substrates of cognitive agi...Cognitive decline is a feature of normal and pathological aging. As the proportion of the global aged population continues to grow, it is imperative to understand the molecular and cellular substrates of cognitive aging for therapeutic discovery. This review focuses on the critical role of neural extracellular matrix in the regulation of neuroplasticity underlying learning and memory in another under-investigated "critical period": the aging process. The fascinating ideas of neural extracellular matrix forming a synaptic cradle in the tetrapartite synapse and possibly serving as a substrate for storage of very long-term memories will be introduced. We emphasize the distinct functional roles of diffusive neural extracellular matrix and perineuronal nets and the advantage of the coexistence of two structures for the adaptation to the ever-changing external and internal environments. Our study of striatal neural extracellular matrix supports the idea that chondroitin sulfate proteoglycan-associated extracellular matrix is restrictive on synaptic neuroplasticity, which plays important functional and pathogenic roles in early postnatal synaptic consolidation and aging-related cognitive decline. Therefore, the chondroitin sulfate proteoglycan-associated neural extracellular matrix can be targeted for normal and pathological aging. Future studies should focus on the cell-type specificity of neural extracellular matrix to identify the endogenous, druggable targets to restore juvenile neuroplasticity and confer a therapeutic benefit to neural circuits affected by aging.展开更多
Neural cells differentiated from pluripotent stem cells(PSCs), including both embryonic stem cells and induced pluripotent stem cells, provide a powerful tool for drug screening, disease modeling and regenerative medi...Neural cells differentiated from pluripotent stem cells(PSCs), including both embryonic stem cells and induced pluripotent stem cells, provide a powerful tool for drug screening, disease modeling and regenerative medicine. High-purity oligodendrocyte progenitor cells(OPCs) and neural progenitor cells(NPCs) have been derived from PSCs recently due to the advancements in understanding the developmental signaling pathways. Extracellular matrices(ECM) have been shown to play important roles in regulating the survival, proliferation, and differentiation of neural cells. To improve the function and maturation of the derived neural cells from PSCs, understanding the effects of ECM over the course of neural differentiation of PSCs is critical. During neural differentiation of PSCs, the cells are sensitive to the properties of natural or synthetic ECMs, including biochemical composition, biomechanical properties, and structural/topographical features. This review summarizes recent advances in neural differentiation of humanPSCs into OPCs and NPCs, focusing on the role of ECM in modulating the composition and function of the differentiated cells. Especially, the importance of using three-dimensional ECM scaffolds to simulate the in vivo microenvironment for neural differentiation of PSCs is highlighted. Future perspectives including the immediate applications of PSC-derived neural cells in drug screening and disease modeling are also discussed.展开更多
Fuzzy cellular neural networks （FCNNs） are special kinds of cellular neural networks （CNNs）. Each cell in an FCNN contains fuzzy operating abilities. The entire network is governed by cellular computing laws. The ...Fuzzy cellular neural networks （FCNNs） are special kinds of cellular neural networks （CNNs）. Each cell in an FCNN contains fuzzy operating abilities. The entire network is governed by cellular computing laws. The design of FCNNs is based on fuzzy local rules. In this paper, a linear matrix inequality （LMI） approach for synchronization control of FCNNs with mixed delays is investigated. Mixed delays include discrete time-varying delays and unbounded distributed delays. A dynamic control scheme is proposed to achieve the synchronization between a drive network and a response network. By constructing the Lyapunov-Krasovskii functional which contains a triple-integral term and the free-weighting matrices method an improved delay-dependent stability criterion is derived in terms of LMIs. The controller can be easily obtained by solving the derived LMIs. A numerical example and its simulations are presented to illustrate the effectiveness of the proposed method.展开更多
The global stability problem of Takagi-Sugeno（T-S） fuzzy Hopfield neural networks（FHNNs） with time delays is investigated.Novel LMI-based stability criteria are obtained by using Lyapunov functional theory to guar...The global stability problem of Takagi-Sugeno（T-S） fuzzy Hopfield neural networks（FHNNs） with time delays is investigated.Novel LMI-based stability criteria are obtained by using Lyapunov functional theory to guarantee the asymptotic stability of the FHNNs with less conservatism.Firstly,using both Finsler＇s lemma and an improved homogeneous matrix polynomial technique,and applying an affine parameter-dependent Lyapunov-Krasovskii functional,we obtain the convergent LMI-based stability criteria.Algebraic properties of the fuzzy membership functions in the unit simplex are considered in the process of stability analysis via the homogeneous matrix polynomials technique.Secondly,to further reduce the conservatism,a new right-hand-side slack variables introducing technique is also proposed in terms of LMIs,which is suitable to the homogeneous matrix polynomials setting.Finally,two illustrative examples are given to show the efficiency of the proposed approaches.展开更多
This paper is concerned with the synchronization of delayed neural networks via sampled-data control. A new technique, namely, the free-matrix-based time-dependent discontinuous Lyapunov functional approach, is adopte...This paper is concerned with the synchronization of delayed neural networks via sampled-data control. A new technique, namely, the free-matrix-based time-dependent discontinuous Lyapunov functional approach, is adopted in constructing the Lyapunov functional, which takes advantage of the sampling characteristic of sawtooth input delay. Based on this discontinuous Lyapunov functional, some less conservative synchronization criteria are established to ensure that the slave system is synchronous with the master system. The desired sampled-data controller can be obtained through the use of the linear matrix inequality（LMI） technique. Finally, two numerical examples are provided to demonstrate the effectiveness and the improvements of the proposed methods.展开更多
This paper studies the problem of linear matrix inequality （LMI） approach to robust stability analysis for stochastic neural networks with a time-varying delay. By developing a delay decomposition approach, the info...This paper studies the problem of linear matrix inequality （LMI） approach to robust stability analysis for stochastic neural networks with a time-varying delay. By developing a delay decomposition approach, the information of the delayed plant states can be taken into full consideration. Based on the new Lyapunov-Krasovskii functional, some inequality techniques and stochastic stability theory, new delay-dependent stability criteria are obtained in terms of LMIs. The proposed results prove the less conservatism, which are realized by choosing new Lyapunov matrices in the decomposed integral intervals. Finally, numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed LMI method.展开更多
The main pathophysiology of cerebral ischemia is the structural alteration in the neurovascular unit, coinciding with neurovascular matrix degradation. Resveratrol has been reported to be one of the most potent chemop...The main pathophysiology of cerebral ischemia is the structural alteration in the neurovascular unit, coinciding with neurovascular matrix degradation. Resveratrol has been reported to be one of the most potent chemopreventive agents that can inhibit cellular processes associated with ischemic stroke. Matrix metalloproteinases （MMPs） has been considered as a potential drug target for the treatment of cerebral ischemia. To explore this, we tried to investigate the inter-action of resveratrol with MMPs through molecular docking studies. At 30 minutes before and 2 hours after cerebral ischemia/reperfusion induced by occlusion of the middle cerebral artery, 40 mg/kg resveratrol was intraperitoneally administered. After resveratrol administration, neu-rological function and brain edema were significantly alleviated, cerebral infarct volume was signiifcantly reduced, and nitrite and malondialdehyde levels in the cortical and striatal regions were signiifcantly decreased. The molecular docking study of resveratrol and MMPs revealed that resveratrol occupied the active site of MMP-2 and MMP-9. The binding energy of the complexes was –37.848672 kJ/mol and –36.6345 kJ/mol for MMP-2 and MMP-9, respectively. In case of MMP-2, Leu 164, Ala 165 and Thr 227 were engaged in H-Bonding with resveratrol and in case of MMP-9, H-bonding was found with Glu 402, Ala 417 and Arg 424 residues. These ifndings collectively reveal that resveratrol exhibits neuroprotective effects on cerebral ischemia through inhibiting MMP-2 and MMP-9 activity.展开更多
Performance robustness problems via the state feedback controller are investigated for a class of uncertain nonlinear systems with time-delay in both state and control, in which the neural networks are used to model t...Performance robustness problems via the state feedback controller are investigated for a class of uncertain nonlinear systems with time-delay in both state and control, in which the neural networks are used to model the nonlinearities. By using an appropriate uncertainty description and the linear difference inclusion technique, sufficient conditions for existence of such controller are derived based on the linear matrix inequalities （LMIs）. Using solutions of LMIs, a state feedback control law is proposed to stabilize the perturbed system and guarantee an upper bound of system performance, which is applicable to arbitrary time-delays.展开更多
Steel strips are the main of steel products and flatness is an important quality indicator of steel strips. Flatness control is the key and highly difficult technique of strip mills. The bottle-neck restricting the im...Steel strips are the main of steel products and flatness is an important quality indicator of steel strips. Flatness control is the key and highly difficult technique of strip mills. The bottle-neck restricting the improvement of flatness control techniques is that the research on flatness theories and control mathematic models is not in accordance with the requirement of technique developments. To build a simple, rapid and accurate explicit formulation control model has become an urgent need for the development of flatness control technique. This paper puts forward the conception of dynamic effective matrix based on the effective matrix method for flatness control proposed by the authors under the consideration of the influence of the change of parameters in roiling processes on the effective matrix, and the concept is validated by industrial productions. Three methods of the effective matrix generation are induced： the calculation method based on the flatness prediction model; the calculation method based on the data excavation in rolling processes and the direct calculation method based on the network model. A fuzzy neural network effective matrix model is built based on the clusters, and then the network structure is optimized and the high-speed-calculation problem of the dynamic effective matrix is solved. The flatness control scheme for cold strip mills is proposed based on the dynamic effective matrix. On stand 5 of the 1 220 mm five-stand 4-high cold strip tandem mill, the industrial experiment with the control methods of tilting roll and bending roll is done by the control scheme of the static effective matrix and the dynamic effective matrix, respectively. The experiment result proves that the control effect of the dynamic effective matrix is much better than that of the static effective matrix. This paper proposes a new idea and method for the dynamic flatness control in the rolling processes of cold strip mills and develops the theory and model of the flatness control effective matrix method.展开更多
文摘The network characteristic of the central neural system has been widely accepted as a basic fabric form. However,the matrix characteristics of neural network are still not fully understood. If we ignore the matrix characteristics of the neural networks and just pay close attention to its connection mode,we are likely to fall into the theory of mechanical reductionism. This can lead to a problem in representing consciousness in a disadvantageous situation. It can also be a barrier to further improving the global workspace theory. Incomplete elucidation of the mechanisms of consciousness representation can also affect the assessment of the surgical outcome of partial epilepsy with conscious injury. Therefore,this paper reviews the epistemological development of neuroscience. We will initially describe the matrix characteristics of the neural system and their significance to the information processing mechanism,and further explore the role of neural matrix in identifying cases of partial epilepsy with little effect on the resection of the lesion.
文摘The matrix metalloproteinases（MMPs） are a family of zinc-dependent endopeptidases originally characterized as secreted proteases responsible for degrading extracellular matrix proteins.Their canonical role in matrix remodelling is of significant importance in neural development and regeneration,but emerging roles for MMPs,especially in signal transduction pathways,are also of obvious importance in a neural context.Misregulation of MMP activity is a hallmark of many neuropathologies,and members of every branch of the MMP family have been implicated in aspects of neural development and disease.However,while extraordinary research efforts have been made to elucidate the molecular mechanisms involving MMPs,methodological constraints and complexities of the research models have impeded progress.Here we discuss the current state of our understanding of the roles of MMPs in neural development using recent examples and advocate a phylogenetically diverse approach to MMP research as a means to both circumvent the challenges associated with specific model organisms,and to provide a broader evolutionary context from which to synthesize an understanding of the underlying biology.
基金Support by Science Foundation of the Ministry of Posts and Telecommunications
文摘Gate matrix layout problem plays an important role in integrated circuit design, but its optimization is NP-hard. In this paper, typical gate layout problem is analysed and adapted to neural network representation, furthermore the simulated results are given.
基金supported by the National Natural Science Foundation of China (No.40574059)the Ministry of Education (No.NCET-04-0904)
文摘In this paper we discuss neural network-based matrix effect correction in energy dispersive X-ray fluorescence (EDXRF) analysis,with detailed algorithm to classify the samples.The method can correct the matrix effect effectively through classifying the samples automatically,and influence of X-ray absorption and enhancement by major elements of the samples is reduced.Experiments for the complex matrix effect correction in EDXRF analysis of samples in Pangang showed improved accuracy of the elemental analysis result.
基金Project supported by the National Natural Science Foundation of China （Grant No 60674026）, the Science Foundation of Southern Yangtze University, China.
文摘In this paper, a synchronization scheme for a class of chaotic neural networks with time-varying delays is presented. This class of chaotic neural networks covers several well-known neural networks, such as Hopfield neural networks, cellular neural networks, and bidirectional associative memory networks. The obtained criteria are expressed in terms of linear matrix inequalities, thus they can be efficiently verified. A comparison between our results and the previous results shows that our results are less restrictive.
基金supported in part by National Alliance for Research on Schizophrenia & Depression(NARSAD)Young Investigator Grant from Brain Behavorial Research Foundation,No.21365(to XHL)Ike Muslow Predoctoral Fellowship from Louisiana State University Health Sciences Center-Shreveport(to ADR)
文摘Cognitive decline is a feature of normal and pathological aging. As the proportion of the global aged population continues to grow, it is imperative to understand the molecular and cellular substrates of cognitive aging for therapeutic discovery. This review focuses on the critical role of neural extracellular matrix in the regulation of neuroplasticity underlying learning and memory in another under-investigated "critical period": the aging process. The fascinating ideas of neural extracellular matrix forming a synaptic cradle in the tetrapartite synapse and possibly serving as a substrate for storage of very long-term memories will be introduced. We emphasize the distinct functional roles of diffusive neural extracellular matrix and perineuronal nets and the advantage of the coexistence of two structures for the adaptation to the ever-changing external and internal environments. Our study of striatal neural extracellular matrix supports the idea that chondroitin sulfate proteoglycan-associated extracellular matrix is restrictive on synaptic neuroplasticity, which plays important functional and pathogenic roles in early postnatal synaptic consolidation and aging-related cognitive decline. Therefore, the chondroitin sulfate proteoglycan-associated neural extracellular matrix can be targeted for normal and pathological aging. Future studies should focus on the cell-type specificity of neural extracellular matrix to identify the endogenous, druggable targets to restore juvenile neuroplasticity and confer a therapeutic benefit to neural circuits affected by aging.
基金Supported by FSU start up fund and FSU Research Foundation GAP awardpartial support from National Science Foundation,No.1342192
文摘Neural cells differentiated from pluripotent stem cells(PSCs), including both embryonic stem cells and induced pluripotent stem cells, provide a powerful tool for drug screening, disease modeling and regenerative medicine. High-purity oligodendrocyte progenitor cells(OPCs) and neural progenitor cells(NPCs) have been derived from PSCs recently due to the advancements in understanding the developmental signaling pathways. Extracellular matrices(ECM) have been shown to play important roles in regulating the survival, proliferation, and differentiation of neural cells. To improve the function and maturation of the derived neural cells from PSCs, understanding the effects of ECM over the course of neural differentiation of PSCs is critical. During neural differentiation of PSCs, the cells are sensitive to the properties of natural or synthetic ECMs, including biochemical composition, biomechanical properties, and structural/topographical features. This review summarizes recent advances in neural differentiation of humanPSCs into OPCs and NPCs, focusing on the role of ECM in modulating the composition and function of the differentiated cells. Especially, the importance of using three-dimensional ECM scaffolds to simulate the in vivo microenvironment for neural differentiation of PSCs is highlighted. Future perspectives including the immediate applications of PSC-derived neural cells in drug screening and disease modeling are also discussed.
基金supported by No. DST/INSPIRE Fellowship/2010/[293]/dt. 18/03/2011
文摘Fuzzy cellular neural networks （FCNNs） are special kinds of cellular neural networks （CNNs）. Each cell in an FCNN contains fuzzy operating abilities. The entire network is governed by cellular computing laws. The design of FCNNs is based on fuzzy local rules. In this paper, a linear matrix inequality （LMI） approach for synchronization control of FCNNs with mixed delays is investigated. Mixed delays include discrete time-varying delays and unbounded distributed delays. A dynamic control scheme is proposed to achieve the synchronization between a drive network and a response network. By constructing the Lyapunov-Krasovskii functional which contains a triple-integral term and the free-weighting matrices method an improved delay-dependent stability criterion is derived in terms of LMIs. The controller can be easily obtained by solving the derived LMIs. A numerical example and its simulations are presented to illustrate the effectiveness of the proposed method.
基金Project supported by the National Natural Science Foundation of China (Grant No. 60974004)the Natural Science Foundation of Jilin Province,China (Grant No. 201115222)
文摘The global stability problem of Takagi-Sugeno（T-S） fuzzy Hopfield neural networks（FHNNs） with time delays is investigated.Novel LMI-based stability criteria are obtained by using Lyapunov functional theory to guarantee the asymptotic stability of the FHNNs with less conservatism.Firstly,using both Finsler＇s lemma and an improved homogeneous matrix polynomial technique,and applying an affine parameter-dependent Lyapunov-Krasovskii functional,we obtain the convergent LMI-based stability criteria.Algebraic properties of the fuzzy membership functions in the unit simplex are considered in the process of stability analysis via the homogeneous matrix polynomials technique.Secondly,to further reduce the conservatism,a new right-hand-side slack variables introducing technique is also proposed in terms of LMIs,which is suitable to the homogeneous matrix polynomials setting.Finally,two illustrative examples are given to show the efficiency of the proposed approaches.
基金Project supported by the National Natural Science Foundation of China(Grant No.61304064)the Scientific Research Fund of Hunan Provincial Education Department,China(Grant Nos.15B067 and 16C0475)a Discovering Grant from Australian Research Council
文摘This paper is concerned with the synchronization of delayed neural networks via sampled-data control. A new technique, namely, the free-matrix-based time-dependent discontinuous Lyapunov functional approach, is adopted in constructing the Lyapunov functional, which takes advantage of the sampling characteristic of sawtooth input delay. Based on this discontinuous Lyapunov functional, some less conservative synchronization criteria are established to ensure that the slave system is synchronous with the master system. The desired sampled-data controller can be obtained through the use of the linear matrix inequality（LMI） technique. Finally, two numerical examples are provided to demonstrate the effectiveness and the improvements of the proposed methods.
基金supported by the Science Foundation of the Department of Science and Technology,New Delhi,India (Grant No.SR/S4/MS:485/07)
文摘This paper studies the problem of linear matrix inequality （LMI） approach to robust stability analysis for stochastic neural networks with a time-varying delay. By developing a delay decomposition approach, the information of the delayed plant states can be taken into full consideration. Based on the new Lyapunov-Krasovskii functional, some inequality techniques and stochastic stability theory, new delay-dependent stability criteria are obtained in terms of LMIs. The proposed results prove the less conservatism, which are realized by choosing new Lyapunov matrices in the decomposed integral intervals. Finally, numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed LMI method.
文摘The main pathophysiology of cerebral ischemia is the structural alteration in the neurovascular unit, coinciding with neurovascular matrix degradation. Resveratrol has been reported to be one of the most potent chemopreventive agents that can inhibit cellular processes associated with ischemic stroke. Matrix metalloproteinases （MMPs） has been considered as a potential drug target for the treatment of cerebral ischemia. To explore this, we tried to investigate the inter-action of resveratrol with MMPs through molecular docking studies. At 30 minutes before and 2 hours after cerebral ischemia/reperfusion induced by occlusion of the middle cerebral artery, 40 mg/kg resveratrol was intraperitoneally administered. After resveratrol administration, neu-rological function and brain edema were significantly alleviated, cerebral infarct volume was signiifcantly reduced, and nitrite and malondialdehyde levels in the cortical and striatal regions were signiifcantly decreased. The molecular docking study of resveratrol and MMPs revealed that resveratrol occupied the active site of MMP-2 and MMP-9. The binding energy of the complexes was –37.848672 kJ/mol and –36.6345 kJ/mol for MMP-2 and MMP-9, respectively. In case of MMP-2, Leu 164, Ala 165 and Thr 227 were engaged in H-Bonding with resveratrol and in case of MMP-9, H-bonding was found with Glu 402, Ala 417 and Arg 424 residues. These ifndings collectively reveal that resveratrol exhibits neuroprotective effects on cerebral ischemia through inhibiting MMP-2 and MMP-9 activity.
基金This project was supported by the National Natural Science Foundation of China (60574001)Program for New Century Excellent Talents in University (NCET-05-0485).
文摘Performance robustness problems via the state feedback controller are investigated for a class of uncertain nonlinear systems with time-delay in both state and control, in which the neural networks are used to model the nonlinearities. By using an appropriate uncertainty description and the linear difference inclusion technique, sufficient conditions for existence of such controller are derived based on the linear matrix inequalities （LMIs）. Using solutions of LMIs, a state feedback control law is proposed to stabilize the perturbed system and guarantee an upper bound of system performance, which is applicable to arbitrary time-delays.
基金supported by National Natural Science Foundation of China(Grant No. 50675186)Hebei Provincial Major Natural Science Foundation of China (Grant No. E2006001038)
文摘Steel strips are the main of steel products and flatness is an important quality indicator of steel strips. Flatness control is the key and highly difficult technique of strip mills. The bottle-neck restricting the improvement of flatness control techniques is that the research on flatness theories and control mathematic models is not in accordance with the requirement of technique developments. To build a simple, rapid and accurate explicit formulation control model has become an urgent need for the development of flatness control technique. This paper puts forward the conception of dynamic effective matrix based on the effective matrix method for flatness control proposed by the authors under the consideration of the influence of the change of parameters in roiling processes on the effective matrix, and the concept is validated by industrial productions. Three methods of the effective matrix generation are induced： the calculation method based on the flatness prediction model; the calculation method based on the data excavation in rolling processes and the direct calculation method based on the network model. A fuzzy neural network effective matrix model is built based on the clusters, and then the network structure is optimized and the high-speed-calculation problem of the dynamic effective matrix is solved. The flatness control scheme for cold strip mills is proposed based on the dynamic effective matrix. On stand 5 of the 1 220 mm five-stand 4-high cold strip tandem mill, the industrial experiment with the control methods of tilting roll and bending roll is done by the control scheme of the static effective matrix and the dynamic effective matrix, respectively. The experiment result proves that the control effect of the dynamic effective matrix is much better than that of the static effective matrix. This paper proposes a new idea and method for the dynamic flatness control in the rolling processes of cold strip mills and develops the theory and model of the flatness control effective matrix method.
