A measure of s k c e s s for any learning algorithm is how u s e ful it is in a variety of learning situations. Those learning algorithms that support universal function approximation can theoretically h e applied to a very large and interesting class of learning problems. Many kinds of neural network architectures have already been shown to support universal approximation. In this paper, we wi...

Much combinatorial optimisation problems constitute a nonpolynomial (NP) hard optimisation problem, i.e., they can not be solved in polynomial time. One such problem is finding the shortest route between two nodes on a graph. Meta-heuristic algorithms such as A∗ along with mixed-integer programming (MIP) methods are often employed for these problems. Our work demonstrates that it is possible to...

Using the Vibration signatures obtained during the operations as the original data, a mechanical condition monitoring method for vacuum circuit breaker is developed in this paper. The method combined the time-frequency analysis and the condition recognition based on artificial neural network. During preprocessing, the vibration signature was decomposed into individual frequency bands using the ...

A pre-processing design using neural networks is proposed for multiwavelet filters. Various numerical experiments are presented and a comparison is given between neural network pre-processing and a preprocessing for solving linear systems. Neural network pre-processing produces a good approximation for a large number of terms and converges repidly. In memoriam Michihiro Nagase To appear in J. o...

In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechan...

Routing is one of the basic parts of a message passing multiprocessor system. The routing procedure has a great impact on the efficiency of a system. Neural algorithms that are currently in use for computer networks require a large number of neurons. If a specific topology of a multiprocessor network is considered, the number of neurons can be reduced. In this paper a new recurrent neural ne...

In this study, the altitude and yaw angle tracking is considered for a scale model helicopter, mounted on an experimental platform, in the presence of model uncertainties, which may be caused by unmodelled dynamics, or aerodynamical disturbances from the environment. To deal with the uncertainties, approximation-based techniques using neural network (NN) are proposed. In particular, two differe...

In this paper, Levenberg-Marquardt (LM) learning algorithm for a single Integrate-and-Fire Neuron (IFN) is proposed and tested for various applications in which a neural network based on multilayer perceptron is conventionally used. It is found that a single IFN is sufficient for the applications that require a number of neurons in different hidden layers of a conventional neural network. Sever...

In this paper, we introduce a Takagi-Sugeno (TS) fuzzy model which is derived from a typical Multi-Layer Perceptron Neural Network (MLP NN). At first, it is shown that the considered MLP NN can be interpreted as a variety of TS fuzzy model. It is discussed that the utilized Membership Function (MF) in such TS fuzzy model, despite its flexible structure, has some major restrictions. After modify...