Modelling, Simulation and Control of Non-linear Dynamical Systems: An Intelligent Approach Using Soft Computing and Fractal Theory

Computers & Technology

Melin, Patricia

Castillo, Oscar

Programming

CRC-Press

These authors use soft computing techniques and fractal theory in this new approach to mathematical modeling, simulation and control of complexion-linear dynamical systems. First, a new fuzzy-fractal approach to automated mathematical modeling of non-linear dynamical systems is presented. It is illustrated with examples on the PROLOG programming language. Second, a new fuzzy-genetic approach to automated simulation of dynamical systems is presented. It is illustrated with examples in the MATLAB programming language. Third, a new method for model-based adaptive control using a neuro-fussy fractal approach is combined with the methods mentioned above. This method is illustrated with MATLAB. Finally, applications of these new methods are presented, in the areas such as biochemical processes, robotic systems, manufacturing, food industry and chemical processes.

Modelling, Simulation and Control of Non-linear Dynamical Systems (Numerical Insights,)

This book presents an extension of the aggregation operator of the generalized interval type-2 Sugeno integral using generalized type-2 fuzzy logic. This extension enables it to handle higher levels of uncertainty when adding any number of sources and types of information in a wide variety of decision-making applications. The authors also demonstrate that the extended aggregation operator offers better performance than other traditional or extended operators. The book is a valuables reference resource for students and researchers working on theory and applications of fuzzy logic in various areas of application where decision making is performed under high levels of uncertainty, such as pattern recognition, time series prediction, intelligent control and manufacturing.

Extension of the Fuzzy Sugeno Integral Based on Generalized Type-2 Fuzzy Logic (SpringerBriefs in Applied Sciences and Technology)

Martinez, Gabriela E.

Computer Science

Springer

This book is focused on the use of intelligent techniques, such as fuzzy logic, neural networks and bio-inspired algorithms, and their application in medical diagnosis. The main idea is that the proposed method may be able to adapt to medical diagnosis problems in different possible areas of the medicine and help to have an improvement in diagnosis accuracy considering a clinical monitoring of 24 hours or more of the patient. In this book, tests were made with different architectures proposed in the different modules of the proposed model. First, it was possible to obtain the architecture of the fuzzy classifiers for the level of blood pressure and for the pressure load, and these were optimized with the different bio-inspired algorithms (Genetic Algorithm and Chicken Swarm Optimization). Secondly, we tested with a local database of 300 patients and good results were obtained.It is worth mentioning that this book is an important part of the proposed general model; for this reason, we consider that these modules have a good performance in a particular way, but it is advisable to perform more tests once the general model is completed.

Neuro Fuzzy Hybrid Models for Classification in Medical Diagnosis (SpringerBriefs in Computational Intelligence)

Guzmán, Juan Carlos

Prado-Arechiga, German

New Medical Diagnosis Models Based on Generalized Type-2 Fuzzy Logic (SpringerBriefs in Applied Sciences and Technology)

Ontiveros-Robles, Emanuel

This book provides a new model for clustering, classification, and time series prediction by using artificial neural networks to computationally simulate the behavior of the cognitive functions of the brain is presented. This model focuses on the study of intelligent hybrid neural systems and their use in time series analysis and decision support systems. Therefore, through the development of eight case studies, multiple time series related to the following problems are analyzed: traffic accidents, air quality and multiple global indicators (energy consumption, birth rate, mortality rate, population growth, inflation, unemployment, sustainable development, and quality of life). The main contribution consists of a Generalized Type-2 fuzzy integration of multiple indicators (time series) using both supervised and unsupervised neural networks and a set of Type-1, Interval Type-2, and Generalized Type-2 fuzzy systems. The obtained results show the advantages of the proposed model of Generalized Type-2 fuzzy integration of multiple time series attributes. This book is intended to be a reference for scientists and engineers interested in applying type-2 fuzzy logic techniques for solving problems in classification and prediction. We consider that this book can also be used to get novel ideas for new lines of research, or to continue the lines of research proposed by the authors of the book.

Clustering, Classification, and Time Series Prediction by Using Artificial Neural Networks (SpringerBriefs in Applied Sciences and Technology)

Ramirez, Martha

In this book a neural network learning method with type-2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that handle weight adaptation and especially fuzzy weights.The internal operation of the neuron is changed to work with two internal calculations for the activation function to obtain two results as outputs of the proposed method. Simulation results and a comparative study among monolithic neural networks, neural network with type-1 fuzzy weights and neural network with type-2 fuzzy weights are presented to illustrate the advantages of the proposed method.The proposed approach is based on recent methods that handle adaptation of weights using fuzzy logic of type-1 and type-2. The proposed approach is applied to a cases of prediction for the Mackey-Glass (for ô=17) and Dow-Jones time series, and recognition of person with iris biometric measure. In some experiments, noise was applied in different levels to the test data of the Mackey-Glass time series for showing that the type-2 fuzzy backpropagation approach obtains better behavior and tolerance to noise than the other methods.The optimization algorithms that were used are the genetic algorithm and the particle swarm optimization algorithm and the purpose of applying these methods was to find the optimal type-2 fuzzy inference systems for the neural network with type-2 fuzzy weights that permit to obtain the lowest prediction error.

New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks (SpringerBriefs in Computational Intelligence)

Gaxiola, Fernando

Valdez, Fevrier

In this book four new methods are proposed. In the first method the generalized type-2 fuzzy logic is combined with the morphological gra-dient technique. The second method combines the general type-2 fuzzy systems (GT2 FSs) and the Sobel operator; in the third approach the me-thodology based on Sobel operator and GT2 FSs is improved to be applied on color images. In the fourth approach, we proposed a novel edge detec-tion method where, a digital image is converted a generalized type-2 fuzzy image. In this book it is also included a comparative study of type-1, inter-val type-2 and generalized type-2 fuzzy systems as tools to enhance edge detection in digital images when used in conjunction with the morphologi-cal gradient and the Sobel operator. The proposed generalized type-2 fuzzy edge detection methods were tested with benchmark images and synthetic images, in a grayscale and color format.Another contribution in this book is that the generalized type-2 fuzzy edge detector method is applied in the preprocessing phase of a face rec-ognition system; where the recognition system is based on a monolithic neural network. The aim of this part of the book is to show the advantage of using a generalized type-2 fuzzy edge detector in pattern recognition applications.The main goal of using generalized type-2 fuzzy logic in edge detec-tion applications is to provide them with the ability to handle uncertainty in processing real world images; otherwise, to demonstrate that a GT2 FS has a better performance than the edge detection methods based on type-1 and type-2 fuzzy logic systems.

Edge Detection Methods Based on Generalized Type-2 Fuzzy Logic (SpringerBriefs in Computational Intelligence)

Gonzalez, Claudia I. I.

Castro, Juan R.

This book focuses on the fields of hybrid intelligent systems based on fuzzy systems, neural networks, bio-inspired algorithms and time series. This book describes the construction of ensembles of Interval Type-2 Fuzzy Neural Networks models and the optimization of their fuzzy integrators with bio-inspired algorithms for time series prediction. Interval type-2 and type-1 fuzzy systems are used to integrate the outputs of the Ensemble of Interval Type-2 Fuzzy Neural Network models. Genetic Algorithms and Particle Swarm Optimization are the Bio-Inspired algorithms used for the optimization of the fuzzy response integrators. The Mackey-Glass, Mexican Stock Exchange, Dow Jones and NASDAQ time series are used to test of performance of the proposed method. Prediction errors are evaluated by the following metrics: Mean Absolute Error, Mean Square Error, Root Mean Square Error, Mean Percentage Error and Mean Absolute Percentage Error. The proposed prediction model outperforms state of the art methods in predicting the particular time series considered in this work.

Ensembles of Type 2 Fuzzy Neural Models and Their Optimization with Bio-Inspired Algorithms for Time Series Prediction (SpringerBriefs in Computational Intelligence)

Soto, Jesus

This monograph describes new methods for intelligent pattern recognition using soft computing techniques including neural networks, fuzzy logic, and genetic algorithms. Hybrid intelligent systems that combine several soft computing techniques are needed due to the complexity of pattern recognition problems. Hybrid intelligent systems can have different architectures, which have an impact on the efficiency and accuracy of pattern recognition systems, to achieve the ultimate goal of pattern recognition. This book also shows results of the application of hybrid intelligent systems to real-world problems of face, fingerprint, and voice recognition. This monograph is intended to be a major reference for scientists and engineers applying new computational and mathematical tools to intelligent pattern recognition and can be also used as a textbook for graduate courses in soft computing, intelligent pattern recognition, computer vision, or applied artificial intelligence.

Hybrid Intelligent Systems for Pattern Recognition Using Soft Computing: An Evolutionary Approach for Neural Networks and Fuzzy Systems (Studies in Fuzziness and Soft Computing, 172)

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