Numerous models for memristor-based single neuron and its network were proposed by taking memristor as a synaptic weight between the adjacent neurons [22–26] or characterizing the effects of electromagnetic induction and radiation [27–30], which leads to the emergence of more complex dynamical ...
For the network architecture, the bus-based approach is used to largely reduce the number of connection count. Field-ordered updating algorithm is used in place of the typical random-ordered updating due to its added advantage in finding global minimum. As for the implementation, VLSI language ...
Abstract :By theoretical analysis and simulation ,the dynamic behavior of a 4-dimensional memristor -based self -synaptic Hopfield Neural Network (MSHNN )and its simulation implementation were studied.Firstly ,the basic conditions for the MSHNN to produce complex dynamic behavior were analyzed.Secondly...
Simple cycles contain rows from one loop only, and the network topology is a feed-forward chain with feedback to one neuron if the loop-vectors in Sigma are cyclic permutations of each other. For special cases this topology simplifies to a ring with only one feedback. Composite cycles ...
Normally an objective function is defined whichrepresenls the complete status of the network and its set of minima gives the stablestates of the network. The status of any neuron of the network can be updated atrandom limes independent of the other bu t in parallel. Since there are ...
A new fractional-order chaos system of Hopfield neural network and its application in image encryption. Chaos Solitons Fractals 2022, 157, 111889. [Google Scholar] [CrossRef] Yu, F.; Shen, H.; Yu, Q.; Kong, X.; Sharma, P.K.; Cai, S. Privacy protection of medical data based on ...
Hopfield neural network is an artificial neural network for storing and retrieving memory similar to the human brain. In the last decades, many works have been devoted to the study of Hopfield type neural networks. For example, periodic and almost periodic solutions [3,4], exponential stability ...
In this paper, we rigorously prove that unpredictable oscillations take place in the dynamics of Hopfield-type neural networks (HNNs) when synaptic connections, rates and external inputs are modulo periodic unpredictable. The synaptic connections, rates
Hopfield neural network is an artificial neural network for storing and retrieving memory similar to the human brain. In the last decades, many works have been devoted to the study of Hopfield type neural networks. For example, periodic and almost periodic solutions [3,4], exponential stability ...
However, optimal networks topology and implementation technology have not yet been selected (the generalizability of networks is not well understood, and there is a lack of explanation for the relationship between the network topology and performance [19]. Nevertheless, we claim that ANN should ...