Genetic programming and bacterial algorithm for neural networks and fuzzy systems design

In the field of control systems it is common to use techniques based on model adaptation to carry out control for plants for which mathematical analysis may be intricate. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this line, this paper gives a perspective on the quality of results given by two different biologically connected learning algorithms for the design of B-spline neural networks (BNN) and fuzzy systems (FS). One approach used is the Genetic Programming (GP) for BNN design and the other is the Bacterial Evolutionary Algorithm (BEA) applied for fuzzy rule extraction. Also, the facility to incorporate a multi-objective approach to the GP algorithm is outlined, enabling the designer to obtain models more adequate for their intended use.

Completely supervised training algorithms for B-spline neural networks and neuro-fuzzy systems

Complete supervised training algorithms for B-spline neural networks and fuzzy rule-based systems are discussed. By interducing the relationship between B-spline neural networks and certain types of fuzzy models, training algorithms developed initially for neural networks can be adapted by fuzzy systems.

Prediction of the solar radiation using RBF neural networks and ground-to-sky images

In this study, Artificial Neural Networks are applied to multistep long term solar radiation prediction. The networks are trained as one-step-ahead predictors and iterated over time to obtain multi-step longer term predictions. Auto-regressive and Auto-regressive with exogenous inputs solar radiationmodels are compared, considering cloudiness indices as inputs in the latter case. These indices are obtained through pixel classification of ground-to-sky images. The input-output structure of the neural network models is selected using evolutionary computation methods.

Desenvolvimento de esquemas de ensino baseados na WWW

Dissertação de mestrado, Engenharia de Sistemas e Computação, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2001

Genetic and bacterial programming for B-Spline neural networks design

The design phase of B-spline neural networks is a highly computationally complex task. Existent heuristics have been found to be highly dependent on the initial conditions employed. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this paper, the Bacterial Programming approach is presented, which is based on the replication of the microbial evolution phenomenon. This technique produces an efficient topology search, obtaining additionally more consistent solutions.

Radio over fiber broadband access networks architectures based on wavelength division multiplexing techniques

The recent remarkable growth in bandwidth of both wired optical and wireless access networks supports a burst of new high bandwidth Internet applications such as: peer-topeer file sharing, cloud storage, on-line gaming, video streaming, etc. Within this scenario, the convergence of fixed and wireless access networks offers significant opportunities for network operators to satisfy user demands, and simultaneously reduce the cost of implementing and running separated wireless and wired networks. The integration of wired and wireless network can be accomplished within several scenarios and at several levels. In this thesis we will focus on converged radio over fiber architectures, particularly on two application scenarios: converged optical 60 GHz wireless networks and wireless overlay backhauling over bidirectional colorless wavelength division multiplexing passive optical networks (WDM-PONs). In the first application scenario, optical 60 GHz signal generation using external modulation of an optical carrier by means of lithium niobate (LiNbO3) Mach- Zehnder modulators (MZM) is considered. The performance of different optical modulation techniques, robust against fiber dispersion is assessed and dispersion mitigation strategies are identified. The study is extended to 60 GHz carriers digitally modulated with data and to systems employing subcarrier multiplexed (SCM) mm-wave channels. In the second application scenario, the performance of WDM-PONs employing reflective semiconductor optical amplifiers (RSOAs), transmitting an overlay orthogonal frequency-division multiplexing (OFDM) wireless signal is assessed analytically and experimentally, with the relevant system impairments being identified. It is demonstrated that the intermodulation due to the beating of the baseband signal and wireless signal at the receiver can seriously impair the wireless channel. Performance degradation of the wireless channel caused by the RSOA gain modulation owing to the downstream baseband data is also assessed, and system design guidelines are provided.

Design of neuro-fuzzy models by evolutionary and gradient-based algorithms

All systems found in nature exhibit, with different degrees, a nonlinear behavior. To emulate this behavior, classical systems identification techniques use, typically, linear models, for mathematical simplicity. Models inspired by biological principles (artificial neural networks) and linguistically motivated (fuzzy systems), due to their universal approximation property, are becoming alternatives to classical mathematical models. In systems identification, the design of this type of models is an iterative process, requiring, among other steps, the need to identify the model structure, as well as the estimation of the model parameters. This thesis addresses the applicability of gradient-basis algorithms for the parameter estimation phase, and the use of evolutionary algorithms for model structure selection, for the design of neuro-fuzzy systems, i.e., models that offer the transparency property found in fuzzy systems, but use, for their design, algorithms introduced in the context of neural networks. A new methodology, based on the minimization of the integral of the error, and exploiting the parameter separability property typically found in neuro-fuzzy systems, is proposed for parameter estimation. A recent evolutionary technique (bacterial algorithms), based on the natural phenomenon of microbial evolution, is combined with genetic programming, and the resulting algorithm, bacterial programming, advocated for structure determination. Different versions of this evolutionary technique are combined with gradient-based algorithms, solving problems found in fuzzy and neuro-fuzzy design, namely incorporation of a-priori knowledge, gradient algorithms initialization and model complexity reduction.