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Bioprocess modelling and fault detectionNott, Paul Jonathan King January 1999 (has links)
No description available.
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Local modelling and control of nonlinear systemsFeng, Ming January 2000 (has links)
No description available.
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Research on Robust Fuzzy Neural NetworksWu, Hsu-Kun 19 November 2010 (has links)
In many practical applications, it is well known that data collected inevitably contain one or more anomalous outliers; that is, observations that are well separated from the majority or bulk of the data, or in some fashion deviate from the general pattern of the data. The occurrence of outliers may be due to misplaced decimal points, recording errors, transmission errors, or equipment failure. These outliers can lead to erroneous parameter estimation and consequently affect the correctness and accuracy of the model inference. In order to solve these problems, three robust fuzzy neural networks (FNNs) will be proposed in this dissertation. This provides alternative learning machines when faced with general nonlinear learning problems. Our emphasis will be put particularly on the robustness of these learning machines against outliers. Though we consider only FNNs in this study, the extension of our approach to other neural networks, such as artificial neural networks and radial basis function networks, is straightforward.
In the first part of the dissertation, M-estimators, where M stands for maximum likelihood, frequently used in robust regression for linear parametric regression problems will be generalized to nonparametric Maximum Likelihood Fuzzy Neural Networks (MFNNs) for nonlinear regression problems. Simple weight updating rules based on gradient descent and iteratively reweighted least squares (IRLS) will be derived.
In the second part of the dissertation, least trimmed squares estimators, abbreviated as LTS-estimators, frequently used in robust (or resistant) regression for linear parametric regression problems will be generalized to nonparametric least trimmed squares fuzzy neural networks, abbreviated as LTS-FNNs, for nonlinear regression problems. Again, simple weight updating rules based on gradient descent and iteratively reweighted least squares (IRLS) algorithms will be provided.
In the last part of the dissertation, by combining the easy interpretability of the parametric models and the flexibility of the nonparametric models, semiparametric fuzzy neural networks (semiparametric FNNs) and semiparametric Wilcoxon fuzzy neural networks (semiparametric WFNNs) will be proposed. The corresponding learning rules are based on the backfitting procedure which is frequently used in semiparametric regression.
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GA-based learning algorithms to identify fuzzy rules for fuzzy neural networksAimejalii, K., Dahal, Keshav P., Hossain, M. Alamgir January 2007 (has links)
Yes / Identification of fuzzy rules is an important issue in
designing of a fuzzy neural network (FNN). However,
there is no systematic design procedure at present. In
this paper we present a genetic algorithm (GA) based
learning algorithm to make use of the known membership
function to identify the fuzzy rules form a large set
of all possible rules. The proposed learning algorithm
initially considers all possible rules then uses the
training data and the fitness function to perform ruleselection.
The proposed GA based learning algorithm
has been tested with two different sets of training data.
The results obtained from the experiments are promising
and demonstrate that the proposed GA based
learning algorithm can provide a reliable mechanism
for fuzzy rule selection.
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Study on Ramsay Fuzzy Neural NetworksWu, Tzung-Han 23 June 2008 (has links)
In this thesis, M-estimators with Ramsay¡¦s function used in robust regression theory for linear parametric regression problems will be generalized to nonparametric Ramsay fuzzy neural networks (RFNNs) for nonlinear regression problems. Emphasis is put particularly on the robustness against outliers. This provides alternative learning machines when faced with general nonlinear learning problems. Simple weight updating rules based on incremental gradient descent and iteratively reweighted least squares (IRLS) will be derived. Some numerical examples will be provided to compare the robustness against outliers for usual fuzzy neural networks (FNNs) and the proposed RFNNs. Simulation results show that the RFNNs proposed in this thesis have good robustness against outliers.
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Adaptive Mode Transition Control Architecture with an Application to Unmanned Aerial VehiclesGutierrez Zea, Luis Benigno 21 May 2004 (has links)
In this thesis, an architecture for the adaptive mode transition control of unmanned aerial vehicles (UAV) is presented. The proposed architecture consists of three levels: the highest level is occupied by mission planning routines where information about way points the vehicle must follow is processed. The middle level uses a trajectory generation component to coordinate the task execution and provides set points for low-level stabilizing controllers. The adaptive mode transitioning control algorithm resides at the lowest level of the hierarchy consisting of a mode transitioning controller and the accompanying adaptation mechanism. The mode transition controller is composed of a mode transition manager, a set of local controllers, a set of active control models, a set point filter, a state filter, an automatic trimming mechanism and a dynamic compensation filter. Local controllers operate in local modes and active control models operate in transitions between two local modes. The mode transition manager determines the actual mode of operation of the vehicle based on a set of mode membership functions and activates a local controller or an active control model accordingly. The adaptation mechanism uses an indirect adaptive control methodology to adapt the active control models. For this purpose, a set of plant models based on fuzzy neural networks is trained based on input/output information from the vehicle and used to compute sensitivity matrices providing the linearized models required by the adaptation algorithms. The effectiveness of the approach is verified through software-in-the-loop simulations, hardware-in-the-loop simulations and flight testing.
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Intelligent Learning Algorithms for Active Vibration ControlMadkour, A.A.M., Hossain, M. Alamgir, Dahal, Keshav P. January 2007 (has links)
Yes / This correspondence presents an investigation into the
comparative performance of an active vibration control (AVC) system
using a number of intelligent learning algorithms. Recursive least square
(RLS), evolutionary genetic algorithms (GAs), general regression neural
network (GRNN), and adaptive neuro-fuzzy inference system (ANFIS)
algorithms are proposed to develop the mechanisms of an AVC system.
The controller is designed on the basis of optimal vibration suppression
using a plant model. A simulation platform of a flexible beam system
in transverse vibration using a finite difference method is considered to
demonstrate the capabilities of the AVC system using RLS, GAs, GRNN,
and ANFIS. The simulation model of the AVC system is implemented,
tested, and its performance is assessed for the system identification models
using the proposed algorithms. Finally, a comparative performance of the
algorithms in implementing the model of the AVC system is presented and
discussed through a set of experiments.
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[pt] AUXÍLIO À ANÁLISE DE SÉRIES TEMPORAIS NÃO SAZONAIS USANDO REDES NEURAIS NEBULOSAS / [en] IDENTIFICATION OF NON-SEASONAL TIME SERIES THROUGH FUZZY NEURAL NETWORKSMARIA AUGUSTA SOARES MACHADO 01 December 2005 (has links)
[pt] Observando a dificuldade de batimento (match) dos padrões
de comportamento das funções de autocorrelação e de
autocorrelação parcial teóricas com as respectivas funções
e as autocorrelação e de autocorrelação parcial estimadas
de uma séries temporal, aliada ao fato da dificuldade em
definir um número em específico como delimitador
inequívoco do que seja um lag significativo, tornam clara
a dose de julgamento subjetivo a ser realizado por um
especialista de análise de séries temporais na tomada de
decisão sobre a estrutura de Box & Jenkins adequada a ser
escolhida para modelar o processo estocástico sendo
estudado. A matemática nebulosa permite a criação de
sistemas de inferências nebulosas (inferência dedutiva) e
representa o conhecimento de forma explícita, através de
regras nebulosas, possibilitando, facilmente, o
entendimento do sistema em estudo. Por outro lado, um
modelo de redes neurais representa o conhecimento de forma
implícita, adquirido através de exemplos (dados),
possuindo excelente capacidade de generalização
(inferência indutiva). Esta tese apresenta um sistema
especialista composto de cinco redes neurais nebulosas do
tipo retropropagação para o auxílio na análise de séries
temporais não sazonais. O sistema indica ao usuário a
estrutura mais adequada, dentre as estruturas AR(1), MA
(1), AR(2), MA(2) e ARMA(1,1), tomando como base a menor
distância Euclidiana entre os valores esperados e as
saídas das redes neurais nebulosas. / [en] It is well known the difficulties associated with the
tradicional procedure for model identification of the Box
& Jenkins model through the pattern matching of the
theoretical and estimated ACF and PACF. The decision on
the acceptance of the null hypothesis of zero ACF (or
PACF) for a given lag is based on a strong asymptotic
result, particularly for the PACF, leading, sometimes, to
wrong decisions on the identified order of the models.
The fuzzy logic allows one to infer system governed by
incomplete or fuzzy knowledge (deductive inference) using
a staighforward formulation of the problem via fuzzy
mathematics. On the other hand, the neural network
represent the knowledge in a implicit manner and has a
great generalization capacity (inductive inference).
In this thesis we built a specialist system composed of 5
fuzzy neural networks to help on the automatic
identificationof the following Box & Jenkins ARMA
structure AR(1), MA(1), AR(2), MA(2) and ARMA (1,1),
through the Euclidian distance between the estimated
output of the net and the corresponding patterns of each
one of the five structures.
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Decision support for coordinated road traffic control actionsDahal, Keshav P., Almejalli, Khaled A., Hossain, M. Alamgir 02 October 2012 (has links)
No / Selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task, which requires significant expert knowledge and experience. Also, the application of a control action for solving a local traffic problem could create traffic congestion at different locations in the network because of the strong interrelations between traffic situations at different locations of a road network. Therefore, coordination of control strategies is required to make sure that all available control actions serve the same objective. In this paper, an Intelligent Traffic Control System (ITCS) based on a coordinated-agent approach is proposed to assist the human operator of a road traffic control centre to manage the current traffic state. In the proposed system, the network is divided into sub-networks, each of which has its own associated agent. The agent of the sub-network with an incident reacts with other affected agents in order to select the optimal traffic control action, so that a globally acceptable solution is found. The agent uses an effective way of calculating the control action fitness locally and globally. The capability of the proposed ITCS has been tested for a case study of a part of the traffic network in the Riyadh city of Saudi Arabia. The obtained results show its ability to identify the optimal global control action. (C) 2012 Elsevier B.V. All rights reserved.
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Dynamical Near Optimal Training for Interval Type-2 Fuzzy Neural Network (T2FNN) with Genetic AlgorithmCheng, Martin Chun-Sheng, pjcheng@ozemail.com.au January 2003 (has links)
Type-2 fuzzy logic system (FLS) cascaded with neural network, called type-2 fuzzy neural network (T2FNN), is presented in this paper to handle uncertainty with dynamical optimal learning. A T2FNN consists of type-2 fuzzy linguistic process as the antecedent part and the two-layer interval neural network as the consequent part. A general T2FNN is computational intensive due to the complexity of type 2 to type 1 reduction. Therefore the interval T2FNN is adopted in this paper to simplify the computational process. The dynamical optimal training algorithm for the two-layer consequent part of interval T2FNN is first developed. The stable and optimal left and right learning rates for the interval neural network, in the sense of maximum error reduction, can be derived for each iteration in the training process (back propagation). It can also be shown both learning rates can not be both negative. Further, due to variation of the initial MF parameters, i.e. the spread level of uncertain means or deviations of interval Gaussian MFs, the performance of back propagation training process may be affected. To achieve better total performance, a genetic algorithm (GA) is designed to search better-fit spread rate for uncertain means and near optimal learnings for the antecedent part. Several examples are fully illustrated. Excellent results are obtained for the truck backing-up control and the identification of nonlinear system, which yield more improved performance than those using type-1 FNN.
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