An auto adaptive neuro-fuzzy segmentation and edge detection architecture is presented. This system consists of a multilayer perceptron (MLP)-like network that performs image segmentation by adaptive thresholding of the input image using labels automatically pre-selected by kernel based fuzzy clustering technique. The proposed architecture is feed forward, but unlike the conventional MLP the le...

Feature selection methods have been explored in the literature for the classification techniques, among which correlated feature, information gain, mutual information and chi-square are considered more effective. The leaf images contain inherent noise due to imaging equipment, operating environment and position of the image during image acquisition. In this paper, a method for classification of...

This paper compares, for a microfinance institution, the performance of two individual classification models: Logistic Regression (Logit) and Multi-Layer Perceptron Neural Network (MLP), to evaluate the credit risk problem and discriminate good creditors from bad ones. Credit scoring systems are currently in common use by numerous financial institutions worldwide. However, credit scoring using ...

the aim of this research is the assessment of the relation between rainfall and large scale synoptically patterns at khorasan razavi province. in this study, using adaptive neuro fuzzy inference system, the rainfall estimation has been done from april to june in the area under study. spring rainfall data including the information of 38 synoptic, climatologic and rain gauge stations from 1970 to...

The severely distorting channels limit the use of linear equalizers and the use of the nonlinear equalizers then becomes justifiable. Neural-network-based equalizers, especially the multilayer perceptron (MLP)-based equalizers, are computationally efficient alternative to currently used nonlinear filter realizations, e.g., the Volterra type. The drawback of the MLP-based equalizers is, however,...

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...

| In this paper, we describe important improvements that were recently introduced in our Discriminative Stochastic Segment Model (DSSM) speech recognizer. We propose a new presegmen-tation algorithm and we optimize the structure of the Multi-Layer Perceptron (MLP) that estimates the phone probabilities. Additionally, we describe a cascade MLP combination technique that relaxes the drawbacks of ...

In this paper, load-carrying capacity in steel shear wall (SSW) was estimated using artificial neural networks (ANNs). The SSW parameters including load-carrying capacity (as ANN’s target), plate thickness, thickness of stiffener, diagonal stiffener distance, horizontal stiffener distance and gravity load (as ANN’s inputs) are used in this paper to train the ANNs. 144 samples data of each of th...