Particle swarm optimization for ensembling generation for evidential k-nearest-neighbour classifier
The problem addressed in this paper concerns the ensembling generation for evidential k-nearest-neighbour classifier. An efficient method based on particle swarm optimization (PSO) is here proposed. We improve the performance of the evidential k-nearest-...
A neural networks approach to minority game
The minority game (MG) comes from the so-called “El Farol bar” problem by W.B. Arthur. The underlying idea is competition for limited resources and it can be applied to different fields such as: stock markets, alternative roads between two locations and ...
Adaptive recurrent neural network control using a structure adaptation algorithm
This paper proposes an adaptive recurrent neural network control (ARNNC) system with structure adaptation algorithm for the uncertain nonlinear systems. The developed ARNNC system is composed of a neural controller and a robust controller. The neural ...
A fuzzy neighborhood-based training algorithm for feedforward neural networks
In this work we present a new hybrid algorithm for feedforward neural networks, which combines unsupervised and supervised learning. In this approach, we use a Kohonen algorithm with a fuzzy neighborhood for training the weights of the hidden layers and ...
A genetic algorithm-based artificial neural network model for the optimization of machining processes
Artificial intelligent tools like genetic algorithm, artificial neural network (ANN) and fuzzy logic are found to be extremely useful in modeling reliable processes in the field of computer integrated manufacturing (for example, selecting optimal ...
Multiscale Bayesian texture segmentation using neural networks and Markov random fields
This paper presents a wavelet-based texture segmentation method using multilayer perceptron (MLP) networks and Markov random fields (MRF) in a multi-scale Bayesian framework. Inputs and outputs of MLP networks are constructed to estimate a posterior ...
Identification using ANFIS with intelligent hybrid stable learning algorithm approaches
This paper suggests novel hybrid learning algorithm with stable learning laws for adaptive network based fuzzy inference system (ANFIS) as a system identifier and studies the stability of this algorithm. The new hybrid learning algorithm is based on ...
Combining nearest neighbor data description and structural risk minimization for one-class classification
One-class classification is an important problem with applications in several different areas such as novelty detection, anomaly detection, outlier detection and machine monitoring. In this paper, we propose two novel methods for one-class ...
Machine learning multi-classifiers for peptide classification
In this paper, we study the performance improvement that it is possible to obtain combining classifiers based on different notions (each trained using a different physicochemical property of amino-acids). This multi-classifier has been tested in ...
The behaviour of the multi-layer perceptron and the support vector regression learning methods in the prediction of NO and NO2 concentrations in Szeged, Hungary
The main aim of this paper is to predict NO and NO2 concentrations 4 days in advance by comparing two artificial intelligence learning methods, namely, multi-layer perceptron and support vector machines, on two kinds of spatial embedding of the temporal ...