Data Mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in database process. It is often referred to as supervised learning because the classes are determined before examining the data. In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input ...

The problem of fault detection and isolation (FDI) in cooperative manipulators is addressed. Free-swinging and locked joint faults are detected and isolated by an FDI system based on neural networks. For each arm, a Multilayer Perceptron (MLP) is used to reproduce the dynamics of the fault-free robot. The outputs of each MLP are compared to the real joint velocities in order to generate a resid...

We consider the problem of classifying multiple simultaneous autoregressive (AR) signals based upon the observation of their sum using a multilayer perceptron network (MLP). We propose a method that allows the training of the classiier to be performed on separate AR processes , and uses the Bayesian interpretation of the outputs of a MLP to obtain the maximum a posteriori probability decomposit...

The approach to the design of direct current (DC) motor varies considerably using advanced methods such as artificial intelligence (AI). However, accuracy issues cannot be totally addressed using conventional methods. This paper presents the study on nonlinear autoregressive moving average with exogenous input (NARMAX) model using multilayer perceptron (MLP) neural network for DC motor modeling...

This paper implements integrative methods to predict Pairwise (PW) and Module-Based (MB) protein interactions in Saccharomyces cerevisiae. The predictive ability of combining diverse sets of relatively strong and weak predictive datasets is investigated. Different classification techniques: Naive Bayesian (NB), Multilayer Perceptron (MLP) and K-Nearest Neighbors (KNN) were evaluated. The assess...

Sammon’s mapping is conventionally used for exploratory data projection, and as such is usually inapplicable for classification. In this paper we apply a neural network (NN) implementation of Sammon’s mapping to classification by extracting an arbitrary number of projections. The projection map and classification accuracy of the mapping are compared with those of the auto-associative NN (AANN),...

A signal su ers from nonlinear, linear, and additive distortion when transmitted through a channel. Linear equalizers are commonly used in receivers to compensate for linear channel distortion. As an alternative, nonlinear equalizers have the potential to compensate for all three sources of channel distortion. Previous authors have shown that nonlinear feedforward equalizers based on either mul...

The problem of predicting oil prices is worthy of attention. As oil represents the backbone of the world economy, the goal of this paper is to design a model, which is more accurate. We modeled the prediction process comprising of three steps: feature selection, data partitioning and analyzing the prediction models. Six prediction models namely: Multi-Layered Perceptron (MLP), Sequential Minima...

In this contribution, human face as biometric [1] is considered. Original method of feature extraction from image data is introduced using MLP (multilayer perceptron) and PCA (principal component analysis). This method is used in human face recognition system and results are compared to face recognition system using PCA directly, to a system with direct classification of input images by MLP and...

An enhanced time-delay neural network (TDNN), using time series sensor response data, improved pattern recognition ability of an electronic nose (e-nose) in discriminating four different spices. TDNN was used for analysis of e-nose time series sensor data from 0 to 4 min, while two popular pattern recognition methods, discriminant function analysis (DFA) and multilayer perceptron (MLP) trained ...