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

From Data Topology to a Modular Classifier

This article describes an approach to designing a distributed and modular neural classifier. This approach introduces a new hierarchical clustering that enables one to determine reliable regions in the representation space by exploiting supervised information. A multilayer perceptron is then associated with each of these detected clusters and charged with recognizing elements of the associated cluster while rejecting all others. The obtained global classifier is comprised of a set of cooperating neural networks and completed by a K-nearest neighbor classifier charged with treating elements rejected by all the neural networks. Experimental results for the handwritten digit recognition problem and comparison with neural and statistical nonmodular classifiers are given

Improved sequential and batch learning in neural networks using the tangent plane algorithm

The principal aim of this research is to investigate and develop improved sequential and batch learning algorithms based upon the tangent plane algorithm for artificial neural networks. A secondary aim is to apply the newly developed algorithms to multi-category cancer classification problems in the bio-informatics area, which involves the study of dna or protein sequences, macro-molecular structures, and gene expressions

Intelligent flight control systems

The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms

Ensemble based on randomised neural networks for online data stream regression in presence of concept drift

The big data paradigm has posed new challenges for the Machine Learning algorithms, such as analysing continuous flows of data, in the form of data streams, and dealing with the evolving nature of the data, which cause a phenomenon often referred to in the literature as concept drift. Concept drift is caused by inconsistencies between the optimal hypotheses in two subsequent chunks of data, whereby the concept underlying a given process evolves over time, which can happen due to several factors including change in consumer preference, economic dynamics, or environmental conditions. This thesis explores the problem of data stream regression with the presence of concept drift. This problem requires computationally efficient algorithms that are able to adapt to the various types of drift that may affect the data. The development of effective algorithms for data streams with concept drift requires several steps that are discussed in this research. The first one is related to the datasets required to assess the algorithms. In general, it is not possible to determine the occurrence of concept drift on real-world datasets; therefore, synthetic datasets where the various types of concept drift can be simulated are required. The second issue is related to the choice of the algorithm. The ensemble algorithms show many advantages to deal with concept drifting data streams, which include flexibility, computational efficiency and high accuracy. For the design of an effective ensemble, this research analyses the use of randomised Neural Networks as base models, along with their optimisation. The optimisation of the randomised Neural Networks involves design and tuning hyperparameters which may substantially affect its performance. The optimisation of the base models is an important aspect to build highly accurate and computationally efficient ensembles. To cope with the concept drift, the existing methods either require setting fixed updating points, which may result in unnecessary computations or slow reaction to concept drift, or rely on drifting detection mechanism, which may be ineffective due to the difficulty to detect drift in real applications. Therefore, the research contributions of this thesis include the development of a new approach for synthetic dataset generation, development of a new hyperparameter optimisation algorithm that reduces the search effort and the need of prior assumptions compared to existing methods, the analysis of the effects of randomised Neural Networks hyperparameters, and the development of a new ensemble algorithm based on bagging meta-model that reduces the computational effort over existing methods and uses an innovative updating mechanism to cope with concept drift. The algorithms have been tested on synthetic datasets and validated on four real-world datasets from various application domains

A functional link network based adaptive power system stabilizer

An on-line identifier using Functional Link Network (FLN) and Pole-shift (PS) controller for power system stabilizer (PSS) application are presented in this thesis. To have the satisfactory performance of the PSS controller, over a wide range of operating conditions, it is desirable to adapt PSS parameters in real time. Artificial Neural Networks (ANNs) transform the inputs in a low-dimensional space to high-dimensional nonlinear hidden unit space and they have the ability to model the nonlinear characteristics of the power system. The ability of ANNs to learn makes them more suitable for use in adaptive control techniques. On-line identification obtains a mathematical model at each sampling period to track the dynamic behavior of the plant. The ANN identifier consisting of a Functional link Network (FLN) is used for identifying the model parameters. A FLN model eliminates the need of hidden layer while retaining the nonlinear mapping capability of the neural network by using enhanced inputs. This network may be conveniently used for function approximation with faster convergence rate and lesser computational load. The most commonly used Pole Assignment (PA) algorithm for adaptive control purposes assign the pole locations to fixed locations within the unit circle in the z-plane. It may not be optimum for different operating conditions. In this thesis, PS type of adaptive control algorithm is used. This algorithm, instead of assigning the closed-loop poles to fixed locations within the unit circle in the z-plane, this algorithm assumes that the pole characteristic polynomial of the closed-loop system has the same form as the pole characteristic of the open-loop system and shifts the open-loop poles radially towards the centre of the unit circle in the z-plane by a shifting factor α according to some rules. In this control algorithm, no coefficients need to be tuned manually, so manual parameter tuning (which is a drawback in conventional power system stabilizer) is minimized. The PS control algorithm uses the on-line updated ARMA parameters to calculate the new closed-loop poles of the system that are always inside the unit circle in the z-plane. Simulation studies on a single-machine infinite bus and on a multi-machine power system for various operating condition changes, verify the effectiveness of the combined model of FLN identifier and PS control in damping the local and multi-mode oscillations occurring in the system. Simulation studies prove that the APSSs have significant benefits over conventional PSSs: performance improvement and no requirement for parameter tuning