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- Single and multi-objective genetic programming design for B-spline neural networks and neuro-fuzzy systemsPublication . Cabrita, Cristiano Lourenço; Ruano, Antonio; Fonseca, C. M.
Expand The design phase of B-spline neural networks and neuro-fuzzy systems is a highly computationally complex task. Existent heuristics, namely the ASMOD algorithm, have been found to be highly dependent on the initial conditions employed. A Genetic Programming approach is proposed, which produces an efficient topology search, obtaining additionally more consistent solutions. The facility to incorporate a multi-objective approach to the GP algorithm is exploited, enabling the designer to obtain better conditioned models, and more adequate for their intended use.Expand - Genetic programming and bacterial algorithm for neural networks and fuzzy systems designPublication . Cabrita, Cristiano Lourenço; Botzheim, J.; Ruano, Antonio; Kóczy, László T.
Expand 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.Expand - Genetic programming and bacterial algorithm for neural networks and fuzzy systems designPublication . Cabrita, Cristiano Lourenço; Botzheim, J.; Ruano, Antonio; Kóczy, László T.
Expand 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.Expand - Supervised training algorithms for B-spline neural networks and fuzzy systemsPublication . Ruano, Antonio; Cabrita, Cristiano Lourenço; Oliveira, J. V.; Tikk, D.; Kóczy, László T.
Expand Complete supervised training algorithms for B-spline neural networks and fuzzy rule-based systems are discussed. By introducing the relationships between B-spline neural networks and Mamdani (satisfying certain assumptions) fuzzy model, training algorithms developed initially for neural networks can be adapted to fuzzy systems. The standard training criterion is reformulated, by separating the linear and nonlinear parameters. By employing this reformulated criterion with the Levenberg-Marquardt algorithm, a new training method, offering a fast rate of convergence is obtained. It is also shown that the standard Error-Back Propagation algorithm, the most common training method for this class of systems, exhibits a very poor performance.Expand - Genetic and bacterial programming for B-spline neural networks designPublication . Ruano, Antonio; Botzheim, J.; Cabrita, Cristiano Lourenço; Kóczy, László T.
Expand 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.Expand - Exploiting the functional training approach in Radial Basis Function networksPublication . Cabrita, Cristiano Lourenço; Ruano, Antonio; Ferreira, P. M.
Expand This paper investigates the application of a novel approach for the parameter estimation of a Radial Basis Function (RBF) network model. The new concept (denoted as functional training) minimizes the integral of the analytical error between the process output and the model output [1]. In this paper, the analytical expressions needed to use this approach are introduced, both for the back-propagation and the Levenberg- Marquardt algorithms. The results show that the proposed methodology outperforms the standard methods in terms of function approximation, serving as an excellent tool for RBF networks training.Expand - A new domain decomposition for B-spline Neural NetworksPublication . Cabrita, Cristiano Lourenço; Ruano, Antonio; Kóczy, László T.
Expand B-spline Neural Networks (BSNNs) belong to the class of networks termed grid or lattice-based associative memories networks (AMN). The grid is a key feature since it allows these networks to exhibit relevant properties which make them efficient in solving problems namely, functional approximation, non-linear system identification, and on-line control. The main problem associated with BSNNs is that the model complexity grows exponentially with the number of input variables. To tackle this drawback, different authors developed heuristics for functional decomposition, such as the ASMOD algorithm or evolutionary approaches [2]. In this paper, we present a complementary approach, by allowing the properties of B-spline models to be achieved by non-full grids. This approach can be applied either to a single model or to an ASMOD decomposition. Simulation results show that comparable results, in terms of approximations can be obtained with less complex models.Expand - Fuzzy rule extraction by bacterial memetic algorithmsPublication . Botzheim, J.; Cabrita, Cristiano Lourenço; Kóczy, László T.; Ruano, Antonio
Expand In our previous papers, fuzzy model identification methods were discussed. The bacterial evolutionary algorithm for extracting fuzzy rule base from a training set was presented. The Levenberg–Marquardt method was also proposed for determining membership functions in fuzzy systems. The combination of the evolutionary and the gradient-based learning techniques is usually called memetic algorithm. In this paper, a new kind of memetic algorithm, the bacterial memetic algorithm, is introduced for fuzzy rule extraction. The paper presents how the bacterial evolutionary algorithm can be improved with the Levenberg–Marquardt technique.Expand - B-spline and neuro-fuzzy models design with function and derivative equalitiesPublication . Cabrita, Cristiano Lourenço; Ruano, Antonio
Expand The design of neuro-fuzzy models is still a complex problem, as it involves not only the determination of the model parameters, but also its structure. Of special importance is the incorporation of a priori information in the design process. In this paper two known design algorithms for B-spline models will be updated to account for function and derivatives equality restrictions, which are important when the neural model is used for performing single or multi-objective optimization on-line.Expand - Programação genética Uni e multiobjectivo para treino de redes neurais B-splinePublication . Cabrita, Cristiano Lourenço
Expand A fase de treino de uma rede neuronal B-spline e sistemas neuro-difusos é uma tarefa extremamente árdua. Algumas heuristicas existentes, nomeadamente o algoritmo ASMOD mostram ser altamente dependentes nas condições iniciais usadas. Deste modo, neste artigo é proposta uma nova estratégia, que pretende protagonizar a procura eficiente da topologia, em conjunto com a obtenção de soluções mais consistentes. A facilidade de incorporação de uma estratégia multiobjectivo também é explorada, permitindo obter modelos melhor condicionados, e mais adequados às intenções expostas.Expand

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