Synapses in biology provide a variety of functions for the neural system. Artificial synaptic electronics that mimic the biological neuron functions are basic building blocks and developing novel artificial synapses i...Synapses in biology provide a variety of functions for the neural system. Artificial synaptic electronics that mimic the biological neuron functions are basic building blocks and developing novel artificial synapses is essential for neuromorphic computation. Inspired by the unique features of biological synapses that the basic connection components of the nervous system and the parallelism, low power consumption, fault tolerance, self-learning and robustness of biological neural systems, artificial synaptic electronics and neuromorphic systems have the potential to overcome the traditional von Neumann bottleneck and create a new paradigm for dealing with complex problems such as pattern recognition, image classification, decision making and associative learning. Nowadays, two-dimensional(2 D) materials have drawn great attention in simulating synaptic dynamic plasticity and neuromorphic computing with their unique properties. Here we describe the basic concepts of bio-synaptic plasticity and learning, the 2 D materials library and its preparation. We review recent advances in synaptic electronics and artificial neuromorphic systems based on 2 D materials and provide our perspective in utilizing 2 D materials to implement synaptic electronics and neuromorphic systems in hardware.展开更多
The goethite iron precipitation process consists of several continuous reactors and involves a series of complex chemical reactions,such as oxidation reaction,hydrolysis reaction and neutralization reaction.It is hard...The goethite iron precipitation process consists of several continuous reactors and involves a series of complex chemical reactions,such as oxidation reaction,hydrolysis reaction and neutralization reaction.It is hard to accurately establish a mathematical model of the process featured by strong nonlinearity,uncertainty and time-delay.A modeling method based on time-delay fuzzy gray cognitive network(T-FGCN)for the goethite iron precipitation process was proposed in this paper.On the basis of the process mechanism,experts’practical experience and historical data,the T-FGCN model of the goethite iron precipitation system was established and the weights were studied by using the nonlinear hebbian learning(NHL)algorithm with terminal constraints.By analyzing the system in uncertain environment of varying degrees,in the environment of high uncertainty,the T-FGCN can accurately simulate industrial systems with large time-delay and uncertainty and the simulated system can converge to steady state with zero gray scale or a small one.展开更多
Goethite iron precipitation process is a key step in direct leaching process of zinc,whose aim is to remove ferrous ions from zinc sulphate solution.The process consists of several cascade reactors,and each of them co...Goethite iron precipitation process is a key step in direct leaching process of zinc,whose aim is to remove ferrous ions from zinc sulphate solution.The process consists of several cascade reactors,and each of them contains complex chemical reactions featured by strong nonlinearity and large time delay.Therefore,it is hard to build up an accurate mathematical model to describe the dynamic changes in the process.In this paper,by studying the mechanism of these reactions and combining historical data and expert experience,the modeling method called asynchronous fuzzy cognitive networks(AFCN)is proposed to solve the various time delay problem.Moreover,the corresponding AFCN model for goethite iron precipitation process is established.To control the process according to fuzzy rules,the nonlinear Hebbian learning algorithm(NHL)terminal constraints is firstly adopted for weights learning.Then the model parameters of equilibrium intervals corresponding to different operating conditions can be calculated.Finally,the matrix meeting the expected value and the weight value of steady states is stored into fuzzy rules as prior knowledge.The simulation shows that the AFCN model for goethite iron precipitation process could precisely describe the dynamic changes in the system,and verifies the superiority of control method based on fuzzy rules.展开更多
基金supported by the National Natural Science Foundation of China(61622401,61851402 and 61734003)the National Key Research and Development Program of China(2017YFB0405600)+2 种基金Shanghai Education Development FoundationShanghai Municipal Education Commission Shuguang Program(18SG01)support from Shanghai Municipal Science and Technology Commission(18JC1410300)
文摘Synapses in biology provide a variety of functions for the neural system. Artificial synaptic electronics that mimic the biological neuron functions are basic building blocks and developing novel artificial synapses is essential for neuromorphic computation. Inspired by the unique features of biological synapses that the basic connection components of the nervous system and the parallelism, low power consumption, fault tolerance, self-learning and robustness of biological neural systems, artificial synaptic electronics and neuromorphic systems have the potential to overcome the traditional von Neumann bottleneck and create a new paradigm for dealing with complex problems such as pattern recognition, image classification, decision making and associative learning. Nowadays, two-dimensional(2 D) materials have drawn great attention in simulating synaptic dynamic plasticity and neuromorphic computing with their unique properties. Here we describe the basic concepts of bio-synaptic plasticity and learning, the 2 D materials library and its preparation. We review recent advances in synaptic electronics and artificial neuromorphic systems based on 2 D materials and provide our perspective in utilizing 2 D materials to implement synaptic electronics and neuromorphic systems in hardware.
基金Project(61673399)supported by the National Natural Science Foundation of ChinaProject(2017JJ2329)supported by the Natural Science Foundation of Hunan Province,ChinaProject(2018zzts550)supported by the Fundamental Research Funds for Central Universities,China
文摘The goethite iron precipitation process consists of several continuous reactors and involves a series of complex chemical reactions,such as oxidation reaction,hydrolysis reaction and neutralization reaction.It is hard to accurately establish a mathematical model of the process featured by strong nonlinearity,uncertainty and time-delay.A modeling method based on time-delay fuzzy gray cognitive network(T-FGCN)for the goethite iron precipitation process was proposed in this paper.On the basis of the process mechanism,experts’practical experience and historical data,the T-FGCN model of the goethite iron precipitation system was established and the weights were studied by using the nonlinear hebbian learning(NHL)algorithm with terminal constraints.By analyzing the system in uncertain environment of varying degrees,in the environment of high uncertainty,the T-FGCN can accurately simulate industrial systems with large time-delay and uncertainty and the simulated system can converge to steady state with zero gray scale or a small one.
基金supported in part by the Program of the National Natural Science Foundation of China under Grant No.61673399in part by the Program of National Natural Science Foundation of Hunan Province under Grant No.2017JJ2329in part by Fundamental Research Funds for Central Universities of Central South University under Grant No.2018zzts550。
文摘Goethite iron precipitation process is a key step in direct leaching process of zinc,whose aim is to remove ferrous ions from zinc sulphate solution.The process consists of several cascade reactors,and each of them contains complex chemical reactions featured by strong nonlinearity and large time delay.Therefore,it is hard to build up an accurate mathematical model to describe the dynamic changes in the process.In this paper,by studying the mechanism of these reactions and combining historical data and expert experience,the modeling method called asynchronous fuzzy cognitive networks(AFCN)is proposed to solve the various time delay problem.Moreover,the corresponding AFCN model for goethite iron precipitation process is established.To control the process according to fuzzy rules,the nonlinear Hebbian learning algorithm(NHL)terminal constraints is firstly adopted for weights learning.Then the model parameters of equilibrium intervals corresponding to different operating conditions can be calculated.Finally,the matrix meeting the expected value and the weight value of steady states is stored into fuzzy rules as prior knowledge.The simulation shows that the AFCN model for goethite iron precipitation process could precisely describe the dynamic changes in the system,and verifies the superiority of control method based on fuzzy rules.
