There are two basic types of artificial neural networks: Multi-Layer Perceptron (MLP) and Radial Basis Function network (RBF). The first type (MLP) consists of one type of neuron, which can be decomposed into a linear and sigmoid part. The second type (RBF) consists of two types of neurons: radial and linear ones. The radial basis function is analyzed and then used for decomposition of RBF network. The resulting Perceptron Radial Basis Function Network (PRBF) consists of two types of neurons: linear and extended sigmoid ones. Any RBF network can be directly converted to a four-layer PRBF network while any MLP network with three layers can be approximated by a five-layer PRBF network. The new PRBF network is then a generalization of MLP and RBF network abilities. Learning strategies are also discussed. The new type of PRBF network and its learning via repeated local optimization is demonstrated on a numerical example together with RBF and MLP for comparison. This paper is organized as follows: Basic properties of MLP and RBF neurons are summarized in the first two chapters. The third chapter includes novel relationship between sigmoidal and radial functions, which is useful for RBF decomposition and generalization. Description of new PRBF network, together with its properties, is subject of the fourth chapter. Numerical experiments with a PRBF and their requests are given in the last chapters.
Text categorization is based on the idea of content-based texts clustering. An Artificial Neural Network (ANN) or simply Neural Network (NN) classifier for Arabic texts categorization is proposed. The Singular Value Decomposition (SVD) is used as preprocessor with the aim of further reducing data in terms of both size and dimensionality. Indeed, the use of SVD makes data more amenable to classification and the convergence training process faster. Specifically, the effectiveness of the Multilayer Perceptron (MLP) and the Radial Basis Function (RBF) classifiers are implemented. Experiments are conducted using an in-house corpus of Arabic texts. Precision, recall and F-measure are used to quantify categorization effectiveness. The results show that the proposed SVD-Supported MLP/RBF ANN classifier is able to achieve high effectiveness. Experimental results also show that the MLP classifier outperforms the RBF classifier and that the SVD-supported NN classifier is better than the basic NN, as far as Arabic text categorization is concerned.
Incipient motion is the critical condition at which bed particles begin to move. Existing relationships for incipient motion prediction do not consider the effect of seepage. Incipient motion design of an alluvial channel affected from seepage requires the information about five basic parameters, i.e., particle size d, water depth y, energy slope Sf, seepage velocity vs and average velocity u. As the process is extremely complex, getting deterministic or analytical form of process phenomena is too difficult. Data mining technique, which is particularly useful in modeling processes about which adequate knowledge of the physics is limited, is presented here as a tool complimentary to model the incipient motion condition of alluvial channel at seepage. This article describes the radial basis function (RBF) network to predict the seepage velocity vs and average velocity u based on experimental data of incipient condition. The prediction capability of model has been found satisfactory and methodology to use the model is also presented. It has been found that model predicts the phenomena very well. With the help of the RBF network, design curves have been presented for designing the alluvial channel when it is affected by seepage. and Návrh aluviálneho kanála s ohľadom na iniciáciu pohybu dna koryta, ovplyvneného priesakom vyžaduje informáciu o piatich základných parametroch: veľkosti častice d, hĺbke vody y, sklone čiary energie Sf, rýchlosti priesaku vs a priemernej rýchlosti prúdenia u. Pretože proces je extrémne zložitý, získať deterministickú alebo analytickú formu riešenia je ťažké. Príspevok opisuje získavanie údajov (data mining technique), bežne používané pri modelovaní. Opisuje aj sieť tzv. ''radial basis function (RBF)'' na prognózu rýchlosti priesaku vs a priemernej rýchlosti u; výpočet je založený na experimentálnych hodnotách v štádiu začínajúceho sa pohybu častíc v koryte. Bola konštatovaná dobrá schopnosť modelu prognózovať začínajúci pohyb; je uvedená tiež metodológia používania modelu. Bolo zistené, že model predpovedá uvedené javy veľmi dobre. Je tu opísaný návrh aluviálneho kanála ovplyvneného priesakom pomocou návrhových kriviek vytvorených pomocou siete RBF.