the main objective in classification of the nmr spectra of cancerous and healthy tissue , with high number of features is the prerequisites of the minimum number of samples. therefore the use of conventional classifier on this type of the data is not recommended. in the current work, different structures of the artificial neural networks (ann) were tried on classification of different cancerous...

abstract: this paper represents a novel use of artificial neural networks in medical science. the proposed technique involves training a multi layer perceptron (mlp) (a kind of artificial neural network) with a bp learning algorithm to recognize a pattern for the diagnosing and prediction of five blood disorders, through the results of blood tests from h1 machine. the blood test parameters and ...

This paper describes a study of the use of four different neural network techniques for automatic speech recognition (ASR) using two common, real-world application databases. The neural network techniques investigated were the Multi-Layer Perceptron (MLP), the Multi-Output-Layer Perceptron (MOLP), which is an improved version of the MLP , the Time-Delay Neural Network (TDNN) and the Kohohnen Se...

in this paper, we introduce a takagi-sugeno (ts) fuzzy model which is derived from a typical multi-layer perceptron neural network (mlp nn). at first, it is shown that the considered mlp nn can be interpreted as a variety of ts fuzzy model. it is discussed that the utilized membership function (mf) in such ts fuzzy model, despite its flexible structure, has some major restrictions. after modify...

Artificial Neural Network (ANN) learns from inputs and outputs. The values of the weights biases in ANN are updated according to Researchers have proposed algorithms train Multi-Layer Perceptron (MLP). However, classical techniques often face problems solving this optimization problem. They tend need large amounts computing time, memory. More importantly, they get stuck within local optimum pro...

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...

Distributed coding at the hidden layer of a multi–layer perceptron (MLP) endows the network with memory compression and noise tolerance capabilities. However, an MLP typically requires slow off–line learning to avoid catastrophic forgetting in an open input environment. An adaptive resonance theory (ART) model is designed to guarantee stable memories even with fast on–line learning. However, AR...

This paper describes incorporating discriminative features from a multi layer perceptron (MLP) into a state-of-the-art Arabic broadcast data transcription system based on cepstral features. The MLP features are based on a recently proposed Bottle-Neck architecture with long-term warped LPTRAP speech representation at the input. It is shown that the previously reported improvements on a developm...

This report examines the fault tolerance of multi-layer perceptron networks. First, the operation of a single perceptron unit is analysed, and it is found that they are highly fault tolerant. This suggests that neural networks composed from these units could in theory be extremely reliable. The multi-layer perceptron network was then examined, but surprisingly was found to be non-fault tolerant...

This paper proposes an utterance veri cation system for hidden Markov model (HMM) based automatic speech recognition systems. A veri cation objective function, based on a multi-layer-perceptron (MLP), is adopted which combines con dence measures from both the recognition and veri cation models. Discriminative minimum veri cation error training is applied for optimizing the parameters of the MLP...