We address a contrastive study between the well known Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) neural networks and a SOM based supervised architecture in a number of data classification tasks. Well known databases like Breast Cancer, Parkinson and Iris were used to evaluate the three architectures by constructing confusion matrices. The results are encouraging and indicate t...

Neural networks have been used to carryout calibration process in fringe projection profilometry for the measurement of three-dimensional object shape. The calibration procedure uses several calibration planes whose positions in space are known. Radial basis function based networks and multi-layer perceptron networks are investigated for the phase recovery. Preliminary studies are also presente...

To estimate the Cation Exchange Capacity (CEC), indirect manner used of Pedotransfer Functions (PTFs). CEC is one of the important soil fertility factors, and not measured directly because it is costly and time consuming. Thus, used from regression equations between easily and non-easily soil properties. The purpose of this research, is develop the PTFs for CEC, with use of easily available soi...

Consider a multilayer perceptron (MLP) with d inputs, a single hidden sigmoidal layer and a linear output. By adding an additional d inputs to the network with values set to the square of the rst d inputs, properties reminiscent of higher-order neural networks and radial basis function networks (RBFN) are added to the architecture with little added expense in terms of weight requirements. Of pa...

One of the current trends in the eld of optical character recognition (OCR) is the combination of multiple classi ers to produce more robust recognition. In this paper, a kNN classi er, a multi-layer perceptron, and a support vector machine with a radial basis function kernel are tested on a pre-segmented word-based OCR task with and without Viterbi errorcorrection. I discuss the effects of err...

Abstract For many problems, the correct behavior of a model depends not only on its input-output mapping but also on properties of its Jacobian matrix, the matrix of partial derivatives of the model’s outputs with respect to its inputs. We introduce the J-prop algorithm, an efficient general method for computing the exact partial derivatives of a variety of simple functions of the Jacobian of a...

the discharge or runoff which ousts from a watershed is important. because its deficiency leads to financial losses and its excesses cause damage in lives and property as flood. in this research using artificial neural network multi-layer perceptron (mlp (and adaptive neuro-fuzzy interface system (anfis) and multiple regression method simulated rainfall- runoff process on daily basis in the kho...

Recently there has been a lot of interest in the extrac tion of symbolic rules from neural networks The work described in this paper is concerned with an evaluation and comparison of the accuracy and complexity of sym bolic rules extracted from radial basis function networks and multi layer perceptrons Here we examine the abil ity of rule extraction algorithms to extract meaningful rules that d...