Total nitrogen has been determined by using a model developed between the conventional chemical measurements and diffuse reflectance spectra in the near-infrared region. Samples (244) from different types of soils with total nitrogen contents ranging from 0.20 to 13.60% (m/m) were modeled by partial least-squares regression (PLS), multi-layer perceptron feed-forward networks (MLP) and radial ba...

A novel method for feature selection and construction is introduced. The method improves the classification accuracy, utilizing the well-established technique of grammatical evolution by creating non-linear mappings of the original features to artificial ones in order to improve the effectiveness of artificial intelligence tools such as multi-layer perceptron (MLP), Radial-basis-function (RBF) ...

This paper presents a comparative study of six soft computing models namely multilayer perceptron networks, Elman recurrent neural network, radial basis function network, Hopfield model, fuzzy inference system and hybrid fuzzy neural network for the hourly electricity demand forecast of Czech Republic. The soft computing models were trained and tested using the actual hourly load data obtained ...

Multilayer perceptrons and radial basis function networks are used most often in classification tasks, even though the two neural networks have different performance in classification tasks depending on the available training data sets. This paper shows the accuracy change in classification of the two neural networks when training data set size changes. Experiments were run with four data sets ...

A study is presented to compare the performance of multilayer perceptrons, radial basis function networks, and probabilistic neural networks for classification. In many classification problems, probabilistic neural networks have outperformed other neural classifiers. Unfortunately, with this kind of networks, the number of required operations to classify one pattern directly depends on the numb...

Supervised Artificial Neural Networks (ANN) are information processing systems that adapt their functionality as a result of exposure to input-output examples. To this end, there exist generic procedures and techniques, known as learning rules. The most widely used in the neural network context rely in derivative information, and are typically associated with the Multilayer Perceptron (MLP). Ot...

It’s well known that the computing time to train multilayer perceptrons is very long because of weight space of the neural networks and small amount of adjustment of the wiights for convergence. The matter becomes worse when the size of training data set is large, which is common in data mining tasks. Moreover, depending on samples, the performance of neural networks change. So, in order to det...

This paper proposes a novel algorithm based on minimizing mutual information for a special case of nonlinear blind source separation: postnonlinear blind source separation. A network composed of a set of radial basis function (RBF) networks, a set of multilayer perceptron and a linear network is used as a demixing system to separate sources in post-nonlinear mixtures. The experimental results s...

The main goal of this paper is to compare the performance which can be achieved by two different hybrid approaches analyzing their applications’ potentiality on real world paradigms (speech recognition and medical diagnosis). We compare the performance obtained with (1) Multinetwork RBF/LVQ structure, we use involves Learning Vector Quantization (LVQ) as a competitive decision processor and Rad...

The \Voice Command" system, designed for isolated word recognition tasks in real-world noisy environments, was implemented on a xed-point DSP board to operate in real-time. Simple auditory model, i.e., zero-crossings with peak amplitudes (ZCPA) model, is used for noise-robust feature extraction , and neural network classiier recognizes input patterns. The system performance is further improved ...