Autoregressive time series prediction by means of fuzzy inference systems using nonparametric residual variance estimation

We propose an automatic methodology framework for short- and long-term prediction of time series by means of fuzzy inference systems. In this methodology, fuzzy techniques and statistical techniques for nonparametric residual variance estimation are combined in order to build autoregressive predictive models implemented as fuzzy inference systems. Nonparametric residual variance estimation plays a key role in driving the identification and learning procedures. Concrete criteria and procedures within the proposed methodology framework are applied to a number of time series prediction problems. The learn from examples method introduced by Wang and Mendel (W&M) is used for identification. The Levenberg–Marquardt (L–M) optimization method is then applied for tuning. The W&M method produces compact and potentially accurate inference systems when applied after a proper variable selection stage. The L–M method yields the best compromise between accuracy and interpretability of results, among a set of alternatives. Delta test based residual variance estimations are used in order to select the best subset of inputs to the fuzzy inference systems as well as the number of linguistic labels for the inputs. Experiments on a diverse set of time series prediction benchmarks are compared against least-squares support vector machines (LS-SVM), optimally pruned extreme learning machine (OP-ELM), and k-NN based autoregressors. The advantages of the proposed methodology are shown in terms of linguistic interpretability, generalization capability and computational cost. Furthermore, fuzzy models are shown to be consistently more accurate for prediction in the case of time series coming from real-world applications.Ministerio de Ciencia e Innovación TEC2008-04920Junta de Andalucía P08-TIC-03674, IAC07-I-0205:33080, IAC08-II-3347:5626

Adaptive nonlinear control using fuzzy logic and neural networks

The problem of adaptive nonlinear control, i.e. the control of nonlinear dynamic systems with unknown parameters, is considered. Current techniques usually assume that either the control system is linearizable or the type of nonlinearity is known. This results in poor control quality for many practical problems. Moreover, the control system design becomes too complex for a practicing engineer. The objective of this thesis is to provide a practical, systematic approach for solving the problem of identification and control of nonlinear systems with unknown parameters, when the explicit linear parametrization is either unknown or impossible. Fuzzy logic (FL) and neural networks (NNs) have proven to be the tools for universal approximation, and hence are considered. However, FL requires expert knowledge and there is a lack of systematic procedures to design NNs for control. A hybrid technique, called fuzzy logic adaptive network (FLAN), which combines the structure of an FL controller with the learning aspects of the NNs is developed. FLAN is designed such that it is capable of both structure learning and parameter learning. Gradient descent based technique is utilized for the parameter learning in FLAN, and it is tested through a variety of simulated experiments in identification and control of nonlinear systems. The results indicate the success of FLAN in terms of accuracy of estimation, speed of convergence, insensitivity against a range of initial learning rates, robustness against sudden changes in the input as well as noise in the training data. The performance of FLAN is also compared with the techniques based on FL and NNs, as well as several hybrid techniques

Identificação difusa de sistemas : proposta de um modelo adaptativo

Tese (Doutorado) - Universidade Federal de Santa Catarina, Programa de Pos-Graduação em Engenharia de ProduçãoIdentificação de sistemas é um processo iterativo, que facilita obter novo conhecimento sobre a natureza do sistema observado a cada nova iteraçao. A identificação de sistemas está vinculada à invenção e avaliação de teorias científicas. O propósito deste trabalho é explorar métodos alternativos para o processo da identificação de sistemas. O modelo proposto é um modelo baseado em regras, que representa as relações entre os agentes do sistema. A incerteza associada aos sistemas é incorporada no modelo via teoria dos conjuntos difusos. A técnica de busca utilizada para descobrir as regras e escolher as funções de pertinência dos conjuntos difusos que otimizam a resposta do modelo, são os algoritmos genéticos. A natureza robusta e os mecanismos simples dos algoritmos genéticos fazem deles uma ferramenta adequada para este propósito. Os algoritmos genéticos são uma técnica baseada nos princípios evolutivos de Darwin. No entanto, biologicamente sempre foi discutido de que forma as adaptações adquiridas por aprendizado durante o tempo de vida de um indivíduo são passadas para seus descendentes. Neste trabalho consideramos estas teorias biológicas e propomos um modelo onde evolução e aprendizado interagem

Neuro-Fuzzy Motion Planning for Robotic Manipulators

On-going research efforts in robotics aim at providing mechanical systems, such as robotic manipulators and mobile robots, with more intelligence so that they can operate autonomously. Advancing in this direction, this thesis proposes and investigates novel manipulator path planning and navigation techniques which have their roots in the field of neural networks and fuzzy logic. Path planning in the configuration space makes necessary a transformation of the workspace into a configuration space. A radial-basis-function neural network is proposed to construct the configuration space by repeatedly mapping individual workspace obstacle points into so-called C-space patterns. The method is extended to compute the transformation for planar manipulators with n links as well as for manipulators with revolute and prismatic joints. A neural-network-based implementation of a computer emulated resistive grid is described and investigated. The grid, which is a collection of nodes laterally connected by weights, carries out global path planning in the manipulator’s configuration space. In response to a specific obstacle constellation, the grid generates an activity distribution whose gradient can be exploited to construct collision-free paths. A novel update algorithm, the To&Fro algorithm, which rapidly spreads the activity distribution over the nodes is proposed. Extensions to the basic grid technique are presented. A novel fuzzy-based system, the fuzzy navigator, is proposed to solve the navigation and obstacle avoidance problem for robotic manipulators. The presented system is divided into separate fuzzy units which individually control each manipulator link. The competing functions of goal following and obstacle avoidance are combined in each unit providing an intelligent behaviour. An on-line reinforcement learning method is introduced which adapts the performance of the fuzzy units continuously to any changes in the environment. All above methods have been tested in different environments on simulated manipulators as well as on a physical manipulator. The results proved these methods to be feasible for real-world applications

Fuzzy rule-based networks for control

Abstract | We present a method for the learning of fuzzy logic membership functions and rules to approximate a numerical function from a set of examples of the function's independent variables and the resulting function value. This method uses a three-step approach to building a complete function approximation system: rst, learning the membership functions and creating a cell-based rule representation; second, simplifying the cell-based rules using an informationtheoretic approach for induction of rules from discrete-valued data; and nally, constructing a computational (neural) network to compute the function value given its independent variables. This function approximation system is demonstrated with a simple control example: learning the truck and trailer backer-upper control system. I

Fuzzy Rule-Based Networks for Control

Abstract | We present a method for the learning of fuzzy logic membership functions and rules to approximate a numerical function from a set of examples of the function's independent variables and the resulting function value. This method uses a three-step approach to building a complete function approximation system: rst, learning the membership functions and creating a cell-based rule representation; second, simplifying the cell-based rules using an informationtheoretic approach for induction of rules from discrete-valued data; and nally, constructing a computational (neural) network to compute the function value given its independent variables. This function approximation system is demonstrated with a simple control example: learning the truck and trailer backer-upper control system. I