This paper presents two neural network approaches to minimum infinity-norm solution of the velocity inverse kinematics problem for redundant robots. Three recurrent neural networks are applied for determining a joint velocity vector with its maximum absolute value component being minimal among all possible joint velocity vectors corresponding to the desired end-effector velocity. In each propos...

This paper gives new simulation results on the asymptotic behaviour of theoretical neural networks on Z and Z(2) following an extended Hopfield law. It specifically focuses on the influence of fixed boundary conditions on such networks. First, we will generalise the theoretical results already obtained for attractive networks in one dimension to more complicated neural networks. Then, we will f...

High capacity associative neural networks can be built from networks of perceptrons, trained using simple perceptron training. Such networks perform much better than those trained using the standard Hopfield one shot Hebbian learning. An experimental investigation into how such networks perform when the connection weights are not free to take any value is reported. The three restrictions invest...

Artificial neural networks are richly connected networks of simple computational elements modeled on biological processes. Systems based on artificial neural networks have high computational rates due to the use of a massive number of these computational elements. Neural networks with feedback connections provide a computing model capable of solving a rich class of optimization problems. In thi...

In this paper, the existence and uniqueness of the equilibrium point and its global asymptotic stability are discussed for a general class of recurrent neural networks with time-varying delays and Lipschitz continuous activation functions. The neural network model considered includes the delayed Hopfield neural networks, bidirectional associative memory networks, and delayed cellular neural net...

We present an algorithm for image segmentation with irregular pyramids. Instead of starting with the original pixel grid, we rst apply some adaptive Voronoi tesselation to the image. This provides the advantage that the number of cells in the bottom level of the pyramid is already reduced as compared to the number of pixels of the original image. Furthermore the Voronoi diagram is a powerful to...

Vehicle Number Plate Recognition system has gained wide popularly with the continuous increase in the number of vehicle related offences. Its research is becoming challenging and interesting day by day. VNPR is designed to help in recognition of number plates of vehicles .Number plate recognition is the term used to unique identify road vehicles without human intervention. VNPR system is a step...

For this paper, we consider the almost sure exponential stability of uncertain stochastic Hopfield neural networks based on subadditive measures. Firstly, deduce two corollaries, using Itô–Liu formula. Then, introduce concept for networks. Next, investigate networks, Lyapunov method, Liu inequality, lemma, and martingale inequality. In addition, prove sufficient conditions stability. Furthermor...