In this paper, we consider the convergence dynamics of Cohen–Grossberg neural networks (CGNNs) with continuously distributed delays. Without assuming the differentiability and monotonicity of activation functions, the differentiability of amplification functions and the symmetry of synaptic interconnection weights, we construct suitable Lyapunov functionals and employ inequality technique to es...

combinatorial optimization is an active field of research in Neural Networks. Since the first attempts to solve the travelling salesman problem with Hopfield nets several progresses have been made. I will present some Neural Network approximate solutions for NP-complete problems that have a sound mathematical foundation and that, beside their theoretical interest, are also numerically encouragi...

‡ Supervised learning. Introduced the idea of a “cost” function over weight space Regression and learning in linear neural networks. The cost was the sum of squared differences between the networks predictions of the correct answers and the correct answers. The motivation was to derive a “learning rule” that adjusts (synaptic) weights to minimize the discrepancy between predictions and answers....

Among the large number of possible optimization algorithms, Hopfield Neural Networks (HNN) propose interesting characteristics for an in-line use. Indeed, this particular optimization algorithm can produce solutions in brief delay. These solutions are produced by the HNN convergence which was originally defined for a sequential evaluation of neurons. While this sequential evaluation leads to lo...

The effect of noise degradation on the Hopfield neural netwerk is s t u d i e d The notion of a hysteresis nefwork is defined. A noisy HephsSi =urd network is subsequently proven to be a hysteresis network. The effect of the hysteresis phentmenon on the robustness of the HoppeM neural network to noise degradation is then investigated. An eptiraal Heefield neural network is defined as the Hopfie...

84 Abstract—In this paper, we analyze the convergence and stability properties of Hopfield Neural Networks (HNN). The global convergence and asymptotic stability of HNN have successful various applications in computing and optimization problems. After determining the mathematical model of the network, we do some analysis on the model. This analysis base on Lyapunov Stability Theorem. Firstly, w...

Since the Lorenz chaotic system was discovered in 1963, construction of systems with complex dynamics has been a research hotspot field chaos. Recently, memristive Hopfield neural networks (MHNNs) offer great potential design complex, because their special network structures, hyperbolic tangent activation function, and memory property. Many based on MHNNs have proposed exhibit various dynamical...