Building a feedforward computational neural network model (CNN) involves two distinct tasks: determination of the network topology and weight estimation. The specification of a problem adequate network topology is a key issue and the primary focus of this contribution. Up to now, this issue has been either completely neglected in spatial application domains, or tackled by search heuristics (see...

There are distinguished two categories of intrusion detection approaches utilizing machine learning according to type of input data. The first one represents network intrusion detection techniques which consider only data captured in network traffic. The second one represents general intrusion detection techniques which intake all possible data sources including host-based features as well as n...

Myocardial infarction is still one of the leading causes of death and morbidity. The early prediction of such disease can prevent or reduce the development of it. Machine learning can be an efficient tool for predicting such diseases. Many people have suffered myocardial infarction in the past. Some of those have survived and others were dead after a period of time. A machine learning system ca...

The focused gamma network is proposed as one of the possible implementations of the gamma neural model. The focused gamma network is compared with the focused backpropagation network and TDNN for a time series prediction problem, and with ADALINE in a system identification problem.

An electronic circuit is presented for a new type of neural network, which gives a recognition rate of over 100 kHz. The network is used to classify handwritten numerals, presented as Fourier and wavelet descriptors, and has been shown to train far quicker than the popular backpropagation network while maintaining classification accuracy.

An algorithm for constructing and training multilayer neural networks, dependence identi cation, is presented in this paper. Its distinctive features are that (i) it transforms the training problem into a set of quadratic optimization problems that are solved by a number of linear equations and (ii) it constructs an appropriate network to meet the training speci cations. The architecture and ne...

Most neural networks used today rely on rigid, fixed-architecture networks and/or slow, gradient descent-based training algorithms (e. g. backpropagation). In this paper, we propose a new neural network learning architecture to counter these problems. Namely, we combine (1) flexible cascade neural networks, which dynamically adjust the size of the neural network as part of the learning process,...

This paper investigates a range of statistical, neural network and hybrid approaches for making one-step-ahead forecasts of a monthly water demand time-series on the basis of 108 historical data points. A uni-variate approach, using solely the water demand time-series, is taken to construct two stand-alone forecasting models: a backpropagation network and a statistical model. A bi-variate appro...