Adaptive systems research is mainly concentrated around optimizing cost functions suitable to problems. Recently, Principe et al. proposed a particle interaction model for information theoretical learning. In this paper, inspired by this idea, we propose a generalization to the particle interaction model for learning and system adaptation. In addition, for the special case of supervised multi-l...

There are a lot of extensions made to the classic model of multi-layer perceptron (MLP). A notable amount of them has been designed to hasten the learning process without considering the quality of generalization. The paper proposes a new MLP extension based on exploiting topology of the input layer of the network. Experimental results show the extended model to improve upon generalization capa...

An autoadaptive neuro-fuzzy segmentation and edge detection architecture is presented. The system consist of a multilayer perceptron (MLP)-like network that performs image segmentation by adaptive thresholding of the input image using labels automatically pre-selected by a fuzzy clustering technique. The proposed architecture is feedforward, but unlike the conventional MLP the learning is unsup...

This paper proposes a hybrid approach based on neuro fuzzy model and emotional learning for prediction of stock exchange market. Neuro fuzzy models are powerful in modeling and forecasting highly nonlinear and complex time series. The emotional Learning, which is successfully used in bounded rational decision making, is introduced as an appropriate method to achieve particular goals in the pred...

Short-term cepstral features have long been chosen as standard features for speaker recognition thanks to their relevance and effectiveness. In contrast, discriminative features, calculated by a multi-layer perceptron (MLP) from much longer stretches of time, have been gradually adopted in automatic speech recognition (ASR). It has been shown that augmenting short-term cepstral features with lo...

In this paper, a channel estimator based on an adaptive neuro fuzzy inference system (ANFIS) is proposed for the purpose of estimating channel frequency responses in orthogonal frequency division multiplexing-interleave division multiple access (OFDM-IDMA) systems. To see the performance of our proposed channel estimation method, five different techniques including well-known pilot-based estima...

The work presents the results of an investigation conducted to compare the performances of the Multi Layer Perceptron (MLP) and the Nearest Neighbor (NN) classifier for handwritten numeral recognition problem. The comparison is drawn in terms of the recognition performance and the computational requirements of the individual classifiers. The results show that a two-layer perceptron performs com...

This paper presents the preliminary analyses towards the development of a formal method for generating autonomous, dynamic ontology systems in the context of web-based audio signals applications. In the music domain, social tags have become important components of database management, recommender systems, and song similarity engines. In this study, we map the audio similarity features from the ...

Deviating from the conventional Hidden Markov ModelMulti-Layer Perceptron (HMM-MLP) hybrid paradigm of using MLP for classi cation, the proposed discriminative MLP technique uses MLP as a mapping module for feature extraction for conventional HMM-based systems. The MLP is discriminatively trained on the phonetically labeled training data to generate the phoneme posterior probabilities. We achie...

A multilayer perceptron is a feed forward artificial neural network model that maps sets of input data onto a set of appropriate output. It is a modification of the standard linear perceptron in that it uses three or more layers of neurons (nodes) with nonlinear activation functions and is more powerful than the perceptron in that it can distinguish data that is not linearly separable, or separ...