#### Filter Results:

- Full text PDF available (14)

#### Publication Year

1969

2012

- This year (0)
- Last 5 years (1)
- Last 10 years (7)

#### Publication Type

#### Co-author

#### Journals and Conferences

#### Key Phrases

Learn More

- Liang Jin, Peter N. Nikiforuk, Madan M. Gupta
- IEEE Trans. Neural Networks
- 1994

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 aā¦ (More)

- Atsushi Fujimori, Peter N. Nikiforuk, Madan M. Gupta
- IEEE Trans. Robotics and Automation
- 1997

- Atsushi Fujimori, Masato Teramoto, Peter N. Nikiforuk, Madan M. Gupta
- J. Field Robotics
- 2000

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 velocityā¦ (More)

- Zeng-Guang Hou, Madan M. Gupta, Peter N. Nikiforuk, Min Tan, Long Cheng
- IEEE Transactions on Neural Networks
- 2007

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 oneā¦ (More)

- Yoshihiro Yamamoto, Peter N. Nikiforuk
- IEEE Trans. Neural Netw. Learning Syst.
- 2000

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 exponentiallyā¦ (More)

Absbaet-In this note, the approximation capability of a class of discrete-time dynamic locurrent neural networks @RN"s) is studied. Analytieal lpsufts presented show that some of the states of sucb a D R " described by a set of dMerence equatbms may be used to approximate uniformly a ate-space trqjectmy pradufed by either a dismte-time nonlinear system or aā¦ (More)

- Liang Jin, Peter N. Nikiforuk, Madan M. Gupta
- IEEE Trans. Systems, Man, and Cybernetics
- 1995

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 modelā¦ (More)

- L. Jin, P. N. Nikiforuk, M. M. Gupta
- 2004

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 neuralā¦ (More)

- Jerzy B. Kiszka, Madan M. Gupta, Peter N. Nikiforuk
- IEEE Trans. Systems, Man, and Cybernetics
- 1985

- Mohammad Vakil, Reza Fotouhi, Peter N. Nikiforuk
- Int. J. Systems Science
- 2011