In this letter,Constructive Neural Networks (CNN) is used in large-scale data mining. By introducing the principle and characteristics of CNN and pointing out its deficiencies,fuzzy theory is adopted to improve the co...In this letter,Constructive Neural Networks (CNN) is used in large-scale data mining. By introducing the principle and characteristics of CNN and pointing out its deficiencies,fuzzy theory is adopted to improve the covering algorithms. The threshold of covering algorithms is redefined. "Extended area" for test samples is built. The inference of the outlier is eliminated. Furthermore,"Sphere Neighborhood (SN)" are constructed. The membership functions of test samples are given and all of the test samples are determined accordingly. The method is used to mine large wireless monitor data (about 3×107 data points),and knowledge is found effectively.展开更多
The intersection of the Industrial Internet of Things(IIoT)and artificial intelligence(AI)has garnered ever-increasing attention and research interest.Nevertheless,the dilemma between the strict resource-constrained n...The intersection of the Industrial Internet of Things(IIoT)and artificial intelligence(AI)has garnered ever-increasing attention and research interest.Nevertheless,the dilemma between the strict resource-constrained nature of IIoT devices and the extensive resource demands of AI has not yet been fully addressed with a comprehensive solution.Taking advantage of the lightweight constructive neural network(LightGCNet)in developing fast learner models for IIoT,a convex geometric constructive neural network with a low-complexity control strategy,namely,ConGCNet,is proposed in this article via convex optimization and matrix theory,which enhances the convergence rate and reduces the computational consumption in comparison with LightGCNet.Firstly,a low-complexity control strategy is proposed to reduce the computational consumption during the hidden parameters training process.Secondly,a novel output weights evaluated method based on convex optimization is proposed to guarantee the convergence rate.Finally,the universal approximation property of ConGCNet is proved by the low-complexity control strategy and convex output weights evaluated method.Simulation results,including four benchmark datasets and the real-world ore grinding process,demonstrate that ConGCNet effectively reduces computational consumption in the modelling process and improves the model’s convergence rate.展开更多
In vitro neural networks offer a simplified model to study brain nervous system functions and represent a vital platform for investigating cerebral neural activities.Microelectrode array(MEA)chips are commonly used to...In vitro neural networks offer a simplified model to study brain nervous system functions and represent a vital platform for investigating cerebral neural activities.Microelectrode array(MEA)chips are commonly used to construct modular neural networks and enable electrical stimulation and recording for uncovering signal generation and conduction mechanisms.However,conventional two-dimensional(2D)MEA chips face significant limitations,including restricted neuronal growth dimensions and insufficient neuron density.Herein,we present a novel micro-integrated chip featuring a three-dimensional(3D)physical microtube array that facilitates the regulated,confined growth of neurons.The microtube array not only provides a 3D microenvironment for neuronal growth and differentiation but also enhances neuronal network density and structural organization.Furthermore,by integrating the microtube array with a customized MEA,precise electrical stimulation can be applied to modular neural networks.Experimental results demonstrate that electrical stimulation effectively promotes the formation of connection pathways between adjacent 3D neural networks.Variable-parameter electrical stimulation experiments reveal that increasing voltage enhances the Young’s modulus of neurons,highlighting the method’s role in supporting the stable development of neuronal networks.This modular culture platform,combined with precise electrical stimulation,paves the way for constructing high-density 3D neuronal networks and enables synchronous control of modular neural activities.The proposed approach holds significant potential for advancing applications in neuroscience,tissue engineering,and organ-onchip technologies.展开更多
基金Supported by the National Natural Science Foundation of China (No.60135010)partially supported by the National Grand Fundamental Research 973 Program of China (No.G1998030509).
文摘In this letter,Constructive Neural Networks (CNN) is used in large-scale data mining. By introducing the principle and characteristics of CNN and pointing out its deficiencies,fuzzy theory is adopted to improve the covering algorithms. The threshold of covering algorithms is redefined. "Extended area" for test samples is built. The inference of the outlier is eliminated. Furthermore,"Sphere Neighborhood (SN)" are constructed. The membership functions of test samples are given and all of the test samples are determined accordingly. The method is used to mine large wireless monitor data (about 3×107 data points),and knowledge is found effectively.
文摘The intersection of the Industrial Internet of Things(IIoT)and artificial intelligence(AI)has garnered ever-increasing attention and research interest.Nevertheless,the dilemma between the strict resource-constrained nature of IIoT devices and the extensive resource demands of AI has not yet been fully addressed with a comprehensive solution.Taking advantage of the lightweight constructive neural network(LightGCNet)in developing fast learner models for IIoT,a convex geometric constructive neural network with a low-complexity control strategy,namely,ConGCNet,is proposed in this article via convex optimization and matrix theory,which enhances the convergence rate and reduces the computational consumption in comparison with LightGCNet.Firstly,a low-complexity control strategy is proposed to reduce the computational consumption during the hidden parameters training process.Secondly,a novel output weights evaluated method based on convex optimization is proposed to guarantee the convergence rate.Finally,the universal approximation property of ConGCNet is proved by the low-complexity control strategy and convex output weights evaluated method.Simulation results,including four benchmark datasets and the real-world ore grinding process,demonstrate that ConGCNet effectively reduces computational consumption in the modelling process and improves the model’s convergence rate.
基金supported by the National Key R&D Program of China(Project No.2022YFB4700100)the Innovation Promotion Research Association of the Chinese Academy of Sciences(NO.2022199)+2 种基金the Applied Basic Research Program of Liaoning Province(NO.2023JH2/101600037)the National Natural Science Foundation of China(Grant No.62403454 and 62303446)the Hong Kong Research Grants Council(NO.11216120).
文摘In vitro neural networks offer a simplified model to study brain nervous system functions and represent a vital platform for investigating cerebral neural activities.Microelectrode array(MEA)chips are commonly used to construct modular neural networks and enable electrical stimulation and recording for uncovering signal generation and conduction mechanisms.However,conventional two-dimensional(2D)MEA chips face significant limitations,including restricted neuronal growth dimensions and insufficient neuron density.Herein,we present a novel micro-integrated chip featuring a three-dimensional(3D)physical microtube array that facilitates the regulated,confined growth of neurons.The microtube array not only provides a 3D microenvironment for neuronal growth and differentiation but also enhances neuronal network density and structural organization.Furthermore,by integrating the microtube array with a customized MEA,precise electrical stimulation can be applied to modular neural networks.Experimental results demonstrate that electrical stimulation effectively promotes the formation of connection pathways between adjacent 3D neural networks.Variable-parameter electrical stimulation experiments reveal that increasing voltage enhances the Young’s modulus of neurons,highlighting the method’s role in supporting the stable development of neuronal networks.This modular culture platform,combined with precise electrical stimulation,paves the way for constructing high-density 3D neuronal networks and enables synchronous control of modular neural activities.The proposed approach holds significant potential for advancing applications in neuroscience,tissue engineering,and organ-onchip technologies.
