introduction: with using multiple linear regression (mlr), can simultaneously analyses several different variables, but to get the desirable results from the mlr, the samples must be much and accurate. therefore, this method has high sensitivity and may cause errors in results. in addition, to use this method, the variable must have normal distribution and modification follow from a linear rela...

A two-layer neural network is developed in which the neuron model computes a user-defined similarity function between inputs and weights. The neuron model is formed by the composition of an adapted logistic function with the mean of the partial input-weight similarities. The model is capable of dealing directly with variables of potentially different nature (continuous, ordinal, categorical); t...

Neural Networks have become very useful tools for input-output knowledge discovery. However, some of the most powerful schemes require very complex machines, and thus a large amount of calculation. This paper presents a general technique to reduce the computational burden associated to the operational phase of most neural networks that calculate their output as a weighted sum of terms, which co...

The two well-known neural network, Hopfield networks and Radial Basis Function networks, have different structures and characteristics. Hopfield neural network and RBF neural network are two of the most commonly-used types of feedback networks and feedforward networks respectively. This study gives an overview for Hopfield neural network and RBF neural network in architectures, the learning pro...

this paper presents the application of radial basis neural networks to the development of a novel method for the condition monitoring and fault diagnosis of synchronous generators. in the proposed scheme, flux linkage analysis is used to reach a decision. probabilistic neural network (pnn) and discrete wavelet transform (dwt) are used in design of fault diagnosis system. pnn as main part of thi...

Input selection is advantageous in regression problems. For example, it might decrease the training time of models, reduce measurement costs, and circumvent problems of high dimensionality. Inclusion of useless inputs into the model increases also the likelihood of overfitting. Neural networks provide good generalization in many cases, but their interpretability is usually limited. However, sel...

Radial neural network can be used to decompose complex problems with good biological properties. Adaptive control for neural network is helpful to improve the efficiency of pattern classification. In order to solve pattern classification with different adaptive characteristics, multiple adaptive algorithms are embedded in radial neural network. Through the test of the objective function, it is ...

The paper examines applicability of Hopfield Model (HFM) for weather forecasting in southern Saskatchewan, Canada. The model performance is contrasted with multi-layered perceptron network (MLPN), Elman recurrent neural network (ERNN) and radial basis function network (RBFN). The data of temperature, wind speed and relative humidity were used to train and test the four models. With each model, ...