Peter N. Nikiforuk

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An analysis of the absolute stability for a general class of discrete-time recurrent neural networks (RNN's) is presented. A discrete-time model of RNN's is represented by a set of nonlinear difference equations. Some sufficient conditions for the absolute stability are derived using Ostrowski's theorem and the similarity transformation approach. For a(More)
This paper presents a new collision avoidance technique, called cooperative collision avoidance, for multiple mobile robots. The detection of the danger of collision between two mobile robots is discussed with respect to the geometric aspects of their paths as are cooperative collision avoidance behaviors. The direction control command and the velocity(More)
A recurrent neural network for the optimal control of a group of interconnected dynamic systems is presented in this paper. On the basis of decomposition and coordination strategy for interconnected dynamic systems, the proposed neural network has a two-level hierarchical structure: several local optimization subnetworks at the lower level and one(More)
A new learning algorithm is presented for supervised learning of multilayered and interconnected neural networks without using a gradient method. First, fictitious teacher signals for the outputs of each hidden unit are algebraically determined by an error backpropagation (EBP) method. Then, the weight parameters are determined by using an exponentially(More)
The problem of learning control for a general class of discrete-time nonlinear systems is addressed in this paper using multilayered neural networks (M"s) with feedforward connections. A suitable extension of the concept of input-output linearization of discrete-time nonlinear systems is used to develop the control schemes for both output tracking and model(More)
A learning and adaptive control scheme for a general class of unknown MIMO discretetime nonlinear systems using multilayered recurrent neural networks (MRNNs) is presented. A novel MRNN structure is proposed to approximate the unknown nonlinear input-output relationship, using a dynamic back propagation (DBP) learning algorithm. Based on the dynamic neural(More)