A novel global hybrid algorithm for feedforward neural networks p. 9 Study on relationship between NIHSS and TCM-SSASD based on the BP neural network multiple models method p. 17 Application of back-propagation neural network to power transformer insulation diagnosis p. 26 Momentum BP neural networks in structural damage detection based on static displacements and natural frequencies p. 35 Defo...

In this paper, two classes of twoand three-dimensional Hopfield-type neural networks with time delays are considered by using a completely different method from known results. Some necessary and sharper sufficient criteria for the global attractivity of the equilibria of the neural networks are present.

This paper studies the problems of global exponential robust stability of high-order hopfield neural networks with time-varying delays. By employing a new Lyapunov-Krasovskii functional and linear matrix inequality, some criteria of global exponential robust stability for the high-order neural networks are established, which are easily verifiable and have a wider adaptive.

Minimization of energy or error functions has proved to be a useful principle in the design and analysis of neural networks and neural algorithms. A brief list of examples include: the backpropagation algorithm, the use of optimization methods in computational vision, the application of analog networks to the approximate solution of NP complete problems and the Hopfield model of associative mem...

This paper investigates delay-dependent robust asymptotic state estimation of fuzzy neural networks with mixed interval time-varying delay. In this paper, the Takagi–Sugeno (T–S) fuzzy model representation is extended to the robust state estimation of Hopfield neural networks with mixed interval time-varying delays. The main purpose is to estimate the neuron states, through available output mea...

This paper presents some new results for the absolute stability of Hopfield neural networks with activation functions chosen from sigmoidal functions which have unbounded derivatives. Detailed discussions are also given to the relation and difference of absolute stabilities between neural networks and Lurie systems with multiple nonlinear controls. Although the basic idea of the absolute stabil...

Many models of neural networks have been extended to complex-valued neural networks. A complex-valued Hopfield neural network (CHNN) is a complex-valued version of a Hopfield neural network. Complex-valued neurons can represent multistates, and CHNNs are available for the storage of multilevel data, such as gray-scale images. The CHNNs are often trapped into the local minima, and their noise to...

In this paper, we show that noise injection into inputs in unsupervised learning neural networks does not improve their performance as it does in supervised learning neural networks. Specifically, we show that training noise degrades the classification ability of a sparsely connected version of the Hopfield neural network, whereas the performance of a sparsely connected winner-take-all neural n...

In this article, we continue our very recent work by extending it to the complex case. Having been inspired real Hopfield neural network (HNN) results, investigations here yield various novel some of which are as follows. First, “biased pseudo-cut” concept HNN (CHNN) case, introduce a “shadow-cut” that is defined sum intercluster phased edges. Second, while discrete-time strictly minimizes in e...