IT has been demonstrated that two reproductive hormones,gonadotropin-releasing hormone(GnRH)and gonadotropin(GTH),exist in the nervous system and Hatschek’s pit oflancelet,a species of Cephalochordata,and that these ...IT has been demonstrated that two reproductive hormones,gonadotropin-releasing hormone(GnRH)and gonadotropin(GTH),exist in the nervous system and Hatschek’s pit oflancelet,a species of Cephalochordata,and that these hormones are involved in the regulationof gonadal development and reproductive activity of lancelet.However,no report could展开更多
In this paper, the method based on uniform design and neural network is proposed to model the complex system. In order to express the system characteristics all round, uniform design method is used to choose the model...In this paper, the method based on uniform design and neural network is proposed to model the complex system. In order to express the system characteristics all round, uniform design method is used to choose the modeling samples and obtain the overall information of the system;for the purpose of modeling the system or its characteristics, the artificial neural network is used to construct the model. Experiment indicates that this method can model the complex system effectively.展开更多
Quasi-Newton, fully-connected, and feed forward neural network were used to study the correlation between the spectral structure of the Eu2+ ion in complex fluorides and the structures of complex fluorides. The experi...Quasi-Newton, fully-connected, and feed forward neural network were used to study the correlation between the spectral structure of the Eu2+ ion in complex fluorides and the structures of complex fluorides. The experimental results show that the neural network architecture (number of layers and number of nodes in each layer),the intial weights. and the organization of the data were all important factors affecting the performance of the neural network. The performance of the neural network was enhanced by adapting a test set to monitor the training process. Once trained, the neural network correctly classified 100% of the training set and 96.3% of the test set. These results offer tremendous improvement over previous pattern recognition methods.展开更多
In this paper, a novel design procedure is proposed for synthesizing high-capacity auto-associative memories based on complex-valued neural networks with real-imaginary-type activation functions and constant delays. S...In this paper, a novel design procedure is proposed for synthesizing high-capacity auto-associative memories based on complex-valued neural networks with real-imaginary-type activation functions and constant delays. Stability criteria dependent on external inputs of neural networks are derived. The designed networks can retrieve the stored patterns by external inputs rather than initial conditions. The derivation can memorize the desired patterns with lower-dimensional neural networks than real-valued neural networks, and eliminate spurious equilibria of complex-valued neural networks. One numerical example is provided to show the effectiveness and superiority of the presented results.展开更多
In this paper, the singularity and its effect on learning dynamics in the complex-valued neural network are elucidated. It has learned that the linear combination structure in the updating rule of the complex-valued n...In this paper, the singularity and its effect on learning dynamics in the complex-valued neural network are elucidated. It has learned that the linear combination structure in the updating rule of the complex-valued neural network increases the speed of moving away from the singular points, and the complex-valued neural network cannot be easily influenced by the singular points, whereas the learning of the usual real-valued neural network can be attracted in the neighborhood of singular points, which causes a standstill in learning. Simulation results on the learning dynamics of the three-layered real-valued and complex-valued neural networks in the neighborhood of singularities support the analytical results.展开更多
In this paper, the multistability issue is discussed for delayed complex-valued recurrent neural networks with discontinuous real-imaginary-type activation functions. Based on a fixed theorem and stability definition,...In this paper, the multistability issue is discussed for delayed complex-valued recurrent neural networks with discontinuous real-imaginary-type activation functions. Based on a fixed theorem and stability definition, sufficient criteria are established for the existence and stability of multiple equilibria of complex-valued recurrent neural networks. The number of stable equilibria is larger than that of real-valued recurrent neural networks, which can be used to achieve high-capacity associative memories. One numerical example is provided to show the effectiveness and superiority of the presented results.展开更多
A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Suge...A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Sugeno's (MTS) fuzzy model and one-order GSNN. Using expectation-maximization(EM) algorithm, parameter estimation and model selection procedures are given. It avoids the shortcomings brought by other methods such as BP algorithm, when the number of parameters is large, BP algorithm is still difficult to apply directly without fine tuning and subjective tinkering. Finally, the simulated example demonstrates the effectiveness.展开更多
In this paper, an adaptive neuro-control structure for complex dynamic system is proposed. A recurrent Neural Network is trained-off-line to learn the inverse dynamics of the system from the observation of the input-o...In this paper, an adaptive neuro-control structure for complex dynamic system is proposed. A recurrent Neural Network is trained-off-line to learn the inverse dynamics of the system from the observation of the input-output data. The direct adaptive approach is performed after the training process is achieved. A lyapunov-Base training algorithm is proposed and used to adjust on-line the network weights so that the neural model output follows the desired one. The simulation results obtained verify the effectiveness of the proposed control method.展开更多
To design approximately linear-phase complex coefficient finite impulse response （FIR） digital filters with arbitrary magnitude and group delay responses, a novel neural network approach is studied. The approach is ...To design approximately linear-phase complex coefficient finite impulse response （FIR） digital filters with arbitrary magnitude and group delay responses, a novel neural network approach is studied. The approach is based on a batch back-propagation neural network algorithm by directly minimizing the real magnitude error and phase error from the linear-phase to obtain the filter＇s coefficients. The approach can deal with both the real and complex coefficient FIR digital filters design problems. The main advantage of the proposed design method is the significant reduction in the group delay error. The effectiveness of the proposed method is illustrated with two optimal design examples.展开更多
This paper investigates the local and global synchronization of a generalized complex dynamical network model with constant and delayed coupling. Without assuming symmetry of the couplings, we proved that a single con...This paper investigates the local and global synchronization of a generalized complex dynamical network model with constant and delayed coupling. Without assuming symmetry of the couplings, we proved that a single controller can pin the generalized complex network to a homogenous solution. Some previous synchronization results are generalized. In this paper, we first discuss how to pin an array of delayed neural networks to the synchronous solution by adding only one controller. Next, by using the Lyapunov functional method, some sufficient conditions are derived for the local and global synchronization of the coupled systems. The obtained results are expressed in terms of LMIs, which can be efficiently checked by the Matlab LMI toolbox. Finally, an example is given to illustrate the theoretical results.展开更多
The aim of the present study was to evaluate whether tissue levels of vitamin B complex and vitamin B12 were altered after crush-induced peripheral nerve injury in an experimental rat model. A total of 80 male Wistar ...The aim of the present study was to evaluate whether tissue levels of vitamin B complex and vitamin B12 were altered after crush-induced peripheral nerve injury in an experimental rat model. A total of 80 male Wistar rats were randomized into one control（n = 8） and six study groups（1, 6, 12, 24 hours, 3, and 7 days after experimental nerve injury; n = 12 for each group）. Crush-induced peripheral nerve injury was performed on the sciatic nerves of rats in six study groups. Tissue samples from the sites of peripheral nerve injury were obtained at 1, 6, 12, 24 hours, 3 and 7 days after experimental nerve injury. Enzyme-linked immunosorbent assay results showed that tissue levels of vitamin B complex and vitamin B12 in the injured sciatic nerve were significantly greater at 1 and 12 hours after experimental nerve injury, while they were significantly lower at 7 days than in control group. Tissue level of vitamin B_（12） in the injured sciatic nerve was significantly lower at 1, 6, 12 and 24 hours than in the control group. These results suggest that tissue levels of vitamin B complex and vitamin B12 vary with progression of crush-induced peripheral nerve injury, and supplementation of these vitamins in the acute period may be beneficial for acceleration of nerve regeneration.展开更多
As the demand for high-quality services proliferates,an innovative network architecture,the fully-decoupled RAN(FD-RAN),has emerged for more flexible spectrum resource utilization and lower network costs.However,with ...As the demand for high-quality services proliferates,an innovative network architecture,the fully-decoupled RAN(FD-RAN),has emerged for more flexible spectrum resource utilization and lower network costs.However,with the decoupling of uplink base stations and downlink base stations in FDRAN,the traditional transmission mechanism,which relies on real-time channel feedback,is not suitable as the receiver is not able to feedback accurate and timely channel state information to the transmitter.This paper proposes a novel transmission scheme without relying on physical layer channel feedback.Specifically,we design a radio map based complex-valued precoding network(RMCPNet)model,which outputs the base station precoding based on user location.RMCPNet comprises multiple subnets,with each subnet responsible for extracting unique modal features from diverse input modalities.Furthermore,the multimodal embeddings derived from these distinct subnets are integrated within the information fusion layer,culminating in a unified representation.We also develop a specific RMCPNet training algorithm that employs the negative spectral efficiency as the loss function.We evaluate the performance of the proposed scheme on the public DeepMIMO dataset and show that RMCPNet can achieve 16%and 76%performance improvements over the conventional real-valued neural network and statistical codebook approach,respectively.展开更多
文摘IT has been demonstrated that two reproductive hormones,gonadotropin-releasing hormone(GnRH)and gonadotropin(GTH),exist in the nervous system and Hatschek’s pit oflancelet,a species of Cephalochordata,and that these hormones are involved in the regulationof gonadal development and reproductive activity of lancelet.However,no report could
文摘In this paper, the method based on uniform design and neural network is proposed to model the complex system. In order to express the system characteristics all round, uniform design method is used to choose the modeling samples and obtain the overall information of the system;for the purpose of modeling the system or its characteristics, the artificial neural network is used to construct the model. Experiment indicates that this method can model the complex system effectively.
文摘Quasi-Newton, fully-connected, and feed forward neural network were used to study the correlation between the spectral structure of the Eu2+ ion in complex fluorides and the structures of complex fluorides. The experimental results show that the neural network architecture (number of layers and number of nodes in each layer),the intial weights. and the organization of the data were all important factors affecting the performance of the neural network. The performance of the neural network was enhanced by adapting a test set to monitor the training process. Once trained, the neural network correctly classified 100% of the training set and 96.3% of the test set. These results offer tremendous improvement over previous pattern recognition methods.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61503338,61573316,61374152,and 11302195)the Natural Science Foundation of Zhejiang Province,China(Grant No.LQ15F030005)
文摘In this paper, a novel design procedure is proposed for synthesizing high-capacity auto-associative memories based on complex-valued neural networks with real-imaginary-type activation functions and constant delays. Stability criteria dependent on external inputs of neural networks are derived. The designed networks can retrieve the stored patterns by external inputs rather than initial conditions. The derivation can memorize the desired patterns with lower-dimensional neural networks than real-valued neural networks, and eliminate spurious equilibria of complex-valued neural networks. One numerical example is provided to show the effectiveness and superiority of the presented results.
文摘In this paper, the singularity and its effect on learning dynamics in the complex-valued neural network are elucidated. It has learned that the linear combination structure in the updating rule of the complex-valued neural network increases the speed of moving away from the singular points, and the complex-valued neural network cannot be easily influenced by the singular points, whereas the learning of the usual real-valued neural network can be attracted in the neighborhood of singular points, which causes a standstill in learning. Simulation results on the learning dynamics of the three-layered real-valued and complex-valued neural networks in the neighborhood of singularities support the analytical results.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.61374094 and 61503338)the Natural Science Foundation of Zhejiang Province,China(Grant No.LQ15F030005)
文摘In this paper, the multistability issue is discussed for delayed complex-valued recurrent neural networks with discontinuous real-imaginary-type activation functions. Based on a fixed theorem and stability definition, sufficient criteria are established for the existence and stability of multiple equilibria of complex-valued recurrent neural networks. The number of stable equilibria is larger than that of real-valued recurrent neural networks, which can be used to achieve high-capacity associative memories. One numerical example is provided to show the effectiveness and superiority of the presented results.
文摘A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Sugeno's (MTS) fuzzy model and one-order GSNN. Using expectation-maximization(EM) algorithm, parameter estimation and model selection procedures are given. It avoids the shortcomings brought by other methods such as BP algorithm, when the number of parameters is large, BP algorithm is still difficult to apply directly without fine tuning and subjective tinkering. Finally, the simulated example demonstrates the effectiveness.
文摘In this paper, an adaptive neuro-control structure for complex dynamic system is proposed. A recurrent Neural Network is trained-off-line to learn the inverse dynamics of the system from the observation of the input-output data. The direct adaptive approach is performed after the training process is achieved. A lyapunov-Base training algorithm is proposed and used to adjust on-line the network weights so that the neural model output follows the desired one. The simulation results obtained verify the effectiveness of the proposed control method.
基金supported by the National Natural Science Foundation of China(6087602250677014)+2 种基金the High-Tech Research and Development Program of China(2006AA04A104)the Hunan Provincial Natural Science Foundation of China (06JJ202407JJ5076).
文摘To design approximately linear-phase complex coefficient finite impulse response （FIR） digital filters with arbitrary magnitude and group delay responses, a novel neural network approach is studied. The approach is based on a batch back-propagation neural network algorithm by directly minimizing the real magnitude error and phase error from the linear-phase to obtain the filter＇s coefficients. The approach can deal with both the real and complex coefficient FIR digital filters design problems. The main advantage of the proposed design method is the significant reduction in the group delay error. The effectiveness of the proposed method is illustrated with two optimal design examples.
基金supported by the National Natural Science Foundation of China (No.60674092)High-tech R & D Program of Jiangsu (Industry)(No.BG2006010)
文摘This paper investigates the local and global synchronization of a generalized complex dynamical network model with constant and delayed coupling. Without assuming symmetry of the couplings, we proved that a single controller can pin the generalized complex network to a homogenous solution. Some previous synchronization results are generalized. In this paper, we first discuss how to pin an array of delayed neural networks to the synchronous solution by adding only one controller. Next, by using the Lyapunov functional method, some sufficient conditions are derived for the local and global synchronization of the coupled systems. The obtained results are expressed in terms of LMIs, which can be efficiently checked by the Matlab LMI toolbox. Finally, an example is given to illustrate the theoretical results.
文摘The aim of the present study was to evaluate whether tissue levels of vitamin B complex and vitamin B12 were altered after crush-induced peripheral nerve injury in an experimental rat model. A total of 80 male Wistar rats were randomized into one control（n = 8） and six study groups（1, 6, 12, 24 hours, 3, and 7 days after experimental nerve injury; n = 12 for each group）. Crush-induced peripheral nerve injury was performed on the sciatic nerves of rats in six study groups. Tissue samples from the sites of peripheral nerve injury were obtained at 1, 6, 12, 24 hours, 3 and 7 days after experimental nerve injury. Enzyme-linked immunosorbent assay results showed that tissue levels of vitamin B complex and vitamin B12 in the injured sciatic nerve were significantly greater at 1 and 12 hours after experimental nerve injury, while they were significantly lower at 7 days than in control group. Tissue level of vitamin B_（12） in the injured sciatic nerve was significantly lower at 1, 6, 12 and 24 hours than in the control group. These results suggest that tissue levels of vitamin B complex and vitamin B12 vary with progression of crush-induced peripheral nerve injury, and supplementation of these vitamins in the acute period may be beneficial for acceleration of nerve regeneration.
基金supported in part by the National Natural Science Foundation Original Exploration Project of China under Grant 62250004the National Natural Science Foundation of China under Grant 62271244+1 种基金the Natural Science Fund for Distinguished Young Scholars of Jiangsu Province under Grant BK20220067the Natural Sciences and Engineering Research Council of Canada (NSERC)
文摘As the demand for high-quality services proliferates,an innovative network architecture,the fully-decoupled RAN(FD-RAN),has emerged for more flexible spectrum resource utilization and lower network costs.However,with the decoupling of uplink base stations and downlink base stations in FDRAN,the traditional transmission mechanism,which relies on real-time channel feedback,is not suitable as the receiver is not able to feedback accurate and timely channel state information to the transmitter.This paper proposes a novel transmission scheme without relying on physical layer channel feedback.Specifically,we design a radio map based complex-valued precoding network(RMCPNet)model,which outputs the base station precoding based on user location.RMCPNet comprises multiple subnets,with each subnet responsible for extracting unique modal features from diverse input modalities.Furthermore,the multimodal embeddings derived from these distinct subnets are integrated within the information fusion layer,culminating in a unified representation.We also develop a specific RMCPNet training algorithm that employs the negative spectral efficiency as the loss function.We evaluate the performance of the proposed scheme on the public DeepMIMO dataset and show that RMCPNet can achieve 16%and 76%performance improvements over the conventional real-valued neural network and statistical codebook approach,respectively.
