Multiobjective RBFNNs Designer for Function Approximation: An Application for Mineral Reduction

Radial Basis Function Neural Networks (RBFNNs) are well known because, among other applications, they present a good perfor- mance when approximating functions. The function approximation prob- lem arises in the construction of a control system to optimize the process of the mineral reduction. In order to regulate the temperature of the ovens and other parameters, it is necessary a module to predict the ¯nal concentration of mineral that will be obtained from the source materials. This module can be formed by an RBFNN that predicts the output and by the algorithm that designs the RBFNN dynamically as more data is obtained. The design of RBFNNs is a very complex task where many parameters have to be determined, therefore, a genetic algorithm that determines all of them has been developed. This algorithm provides sat- isfactory results since the networks it generates are able to predict quite precisely the ¯nal concentration of mineral.Spanish CICYT Project TIN2004-01419European Commission's Research Infrastructures RII3-CT-2003-506079 (HPC-Europa

Radial Basis Function Neural Networks : A Review

Radial Basis Function neural networks (RBFNNs) represent an attractive alternative to other neural network models. One reason is that they form a unifying link between function approximation, regularization, noisy interpolation, classification and density estimation. It is also the case that training RBF neural networks is faster than training multi-layer perceptron networks. RBFNN learning is usually split into an unsupervised part, where center and widths of the Gaussian basis functions are set, and a linear supervised part for weight computation. This paper reviews various learning methods for determining centers, widths, and synaptic weights of RBFNN. In addition, we will point to some applications of RBFNN in various fields. In the end, we name software that can be used for implementing RBFNNs

Robot competence development by constructive learning

This paper presents a constructive learning approach for developing sensor-motor mapping in autonomous systems. The system's adaptation to environment changes is discussed and three methods are proposed to deal with long term and short term changes. The proposed constructive learning allows autonomous systems to develop network topology and adjust network parameters. The approach is supported by findings from psychology and neuroscience especially during infants cognitive development at early stages. A growing radial basis function network is introduced as a computational substrate for sensory-motor mapping learning. Experiments are conducted on a robot eye/hand coordination testbed and results show the incremental development of sensory-motor mapping and its adaptation to changes such as in tool-use

Advances in Multi-Sensor Data Fusion: Algorithms and Applications

With the development of satellite and remote sensing techniques, more and more image data from airborne/satellite sensors have become available. Multi-sensor image fusion seeks to combine information from different images to obtain more inferences than can be derived from a single sensor. In image-based application fields, image fusion has emerged as a promising research area since the end of the last century. The paper presents an overview of recent advances in multi-sensor satellite image fusion. Firstly, the most popular existing fusion algorithms are introduced, with emphasis on their recent improvements. Advances in main applications fields in remote sensing, including object identification, classification, change detection and maneuvering targets tracking, are described. Both advantages and limitations of those applications are then discussed. Recommendations are addressed, including: (1) Improvements of fusion algorithms; (2) Development of “algorithm fusion” methods; (3) Establishment of an automatic quality assessment scheme

Extreme Learning Machine combined with a Differential Evolution algorithm for lithology identification

Lithology identification, obtained through the analysis of several geophysical properties, has an important role in the process of characterization of oil reservoirs. The identification can be accomplished by direct and indirect methods, but these methods are not always feasible because of the cost or imprecision of the results generated. Consequently, there is a need to automate the procedure of reservoir characterization and, in this context, computational intelligence techniques appear as an alternative to lithology identification. However, to acquire proper performance, usually some parameters should be adjusted and this can become a hard task depending on the complexity of the underlying problem. This paper aims to apply an Extreme Learning Machine (ELM) adjusted with a Differential Evolution (DE) to classify data from the South Provence Basin, using a previously published paper as a baseline reference. The paper contributions include the use of an evolutionary algorithm as a tool for search on the hyperparameters of the ELM. In addition, an  activation function recently proposed in the literature is implemented and tested. The  computational approach developed here has the potential to assist in petrographic data classification and helps to improve the process of reservoir characterization and the production development planning

Platonic model of mind as an approximation to neurodynamics

Hierarchy of approximations involved in simplification of microscopic theories, from sub-cellural to the whole brain level, is presented. A new approximation to neural dynamics is described, leading to a Platonic-like model of mind based on psychological spaces. Objects and events in these spaces correspond to quasi-stable states of brain dynamics and may be interpreted from psychological point of view. Platonic model bridges the gap between neurosciences and psychological sciences. Static and dynamic versions of this model are outlined and Feature Space Mapping, a neurofuzzy realization of the static version of Platonic model, described. Categorization experiments with human subjects are analyzed from the neurodynamical and Platonic model points of view

Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques

Intrusions on computer network systems are major security issues these days. Therefore, it is of utmost importance to prevent such intrusions. The prevention of such intrusions is entirely dependent on their detection that is a main part of any security tool such as Intrusion Detection System (IDS), Intrusion Prevention System (IPS), Adaptive Security Alliance (ASA), checkpoints and firewalls. Therefore, accurate detection of network attack is imperative. A variety of intrusion detection approaches are available but the main problem is their performance, which can be enhanced by increasing the detection rates and reducing false positives. Such weaknesses of the existing techniques have motivated the research presented in this thesis. One of the weaknesses of the existing intrusion detection approaches is the usage of a raw dataset for classification but the classifier may get confused due to redundancy and hence may not classify correctly. To overcome this issue, Principal Component Analysis (PCA) has been employed to transform raw features into principal features space and select the features based on their sensitivity. The sensitivity is determined by the values of eigenvalues. The recent approaches use PCA to project features space to principal feature space and select features corresponding to the highest eigenvalues, but the features corresponding to the highest eigenvalues may not have the optimal sensitivity for the classifier due to ignoring many sensitive features. Instead of using traditional approach of selecting features with the highest eigenvalues such as PCA, this research applied a Genetic Algorithm (GA) to search the principal feature space that offers a subset of features with optimal sensitivity and the highest discriminatory power. Based on the selected features, the classification is performed. The Support Vector Machine (SVM) and Multilayer Perceptron (MLP) are used for classification purpose due to their proven ability in classification. This research work uses the Knowledge Discovery and Data mining (KDD) cup dataset, which is considered benchmark for evaluating security detection mechanisms. The performance of this approach was analyzed and compared with existing approaches. The results show that proposed method provides an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates