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<p><b>Provides an in-depth and even treatment of the three pillars of computational intelligence and how they relate to one another</b></p> <p>This book covers the three fundamental topics that form the basis of computational intelligence:&nbsp; neural networks, fuzzy systems, and evolutionary computation. The text focuses on inspiration, design, theory, and practical aspects of implementing procedures to solve real-world problems. While other books in the three fields that comprise computational intelligence are written by specialists in one discipline, this book is co-written by current former Editor-in-Chief of IEEE Transactions on Neural Networks and Learning Systems, a former Editor-in-Chief of IEEE Transactions on Fuzzy Systems, and the founding Editor-in-Chief of IEEE Transactions on Evolutionary Computation. The coverage across the three topics is both uniform and consistent in style and notation.</p> <ul> <li>Discusses single-layer and multilayer neural networks, radial-basis function networks, and recurrent neural networks</li> <li>Covers fuzzy set theory, fuzzy relations, fuzzy logic interference, fuzzy clustering and classification, fuzzy measures and fuzzy integrals</li> <li>Examines evolutionary optimization, evolutionary learning and problem solving, and collective intelligence</li> <li>Includes end-of-chapter practice problems that will help readers apply methods and techniques to real-world problems</li> </ul> <p><i>Fundamentals of Computational intelligence</i> is written for advanced undergraduates, graduate students, and practitioners in electrical and computer engineering, computer science, and other engineering disciplines.</p>

Many computer applications require microprocessors to reliably interconnect and communicate with other peripherals in order to perform their intended functions. Interface design, which includes the development of the methods and processes by which two or more components communicate, is a crucial step in the deployment of microprocessors in an embedded computing environment. ARM-based microprocessors are a leading technology in this field, offering a wide range of performance for different applications. This book provides a comprehensive treatment of interface design from basic logical and theoretical principles to practical implementation on an ARM-based microprocessor, addressing both hardware and software considerations. The microprocessor's high level of complexity is carefully analysed in the text to provide clear guidance for the reader in the design of new applications, resulting in an invaluable reference resource for graduates and engineers involved in the design of electronic products and systems. Key Features: * Brings together aspects of digital hardware, interface design and software integration in a single text to make clear the link between low and high level languages for interface control * Categorises interface techniques into easily distinguished chapters, progressively involving greater complexity, enabling the reader to quickly find relevant material for a particular application * Provides many practical C-coded examples showing both the preparation and use of complex programmable subsystems implemented in a typical commercial product * Presents in each chapter an introduction to the essential theoretical aspects and the development of simple interface designs using basic logical building blocks

\"Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines\" by Stefano Nolfi and Dario Floreano is reviewed.

This chapter focuses on the radial-basis function (RBF) network as an alternative to multilayer perceptrons. It will be interesting to find that in a multilayer perceptron, the function approximation is defined by a nested set of weighted summations, while in a RBF network, the approximation is defined by a single weighted sum. The chapter focuses on the use of a Gaussian function as the radial-basis function. The reason behind the choice of the Gaussian function as the radial-basis function in building RBF networks is that it has many desirable properties, which will become evident as the discussion progresses. It is important to point out that RBF networks and multilayer perceptrons can be trained in alternative ways besides those presented. For multilayer perceptrons, the backpropagation algorithm is simple to compute locally and it performs stochastic gradient descent in weight space when the algorithm is implemented in an online learning mode.

This introduction presents an overview of key concepts discussed in the subsequent chapters of this book. This book offers the only systematic treatment of the entire field of computational intelligence (CI) from the perspectives of the experts who have guided peer-reviewed seminal research published in the toptier journals in the area of CI. The described theories and techniques allow you to create solutions to problems in pattern recognition, control, automated decision making, optimization, statistical modeling, and many other areas. The chapter covers basic and advanced material in neural networks, fuzzy systems, and evolutionary computation. It provides fundamental material in the diverse and fast growing area of CI and gives you a strong fundamental understanding of its basic concepts. The chapter offers exercises to test your knowledge and explores interesting research problems. The book describes each of the three main topics with basic chapters upfront, which cover theory, framework, and algorithms.

Fuzzy set theory and fuzzy logic provide a different way to view the problem of modeling uncertainty and offer a wide range of computational tools to aid decision making. The mathematical basis for formal fuzzy logic can be found in infinite-valued logics, first studied by the Polish logician Jan Lukasiewicz in the 1920s. While the big economic impact of fuzzy set theory and fuzzy logic centers on control, particularly in consumer electronics, there has been, and continues to be, much research and application of these technologies in pattern recognition, information fusion, data mining, and automated decision making. All fuzzy set theory is based on the concept of a membership function. In many cases, the membership functions take on specific functional forms such as triangular, trapezoidal, S-functions, pi-functions, sigmoids, and even Gaussians for convenience in representation and computation. A neural network also acts as a membership function.

This chapter focuses fuzzy clustering with the fuzzy C-means (FCM) and the possibilistic C-means (PCM) as the primary examples. Clustering methods are used to look for structure in sets of unlabeled vectors. In many applications, we need to assign known labels to test data. In these cases, we assume that we have training sets of patterns that represent the various classes under consideration. The chapter shows how both the distances and fuzzy labels are combined to create class labels for the test vector. Like the crisp counterpart, the fuzzy k-nearest neighbor (FKNN) algorithm is simple in concept. The concept of nearest neighbors normally refers to distance in a metric space. In Gader et al., the k-NN soft labels were used to drive a self-organizing feature map (SOFM), a neural-based clustering algorithm. It can be considered the extreme case of the multiprototype classification that is related to clustering.

Neural networks are potentially massively parallel distributed structures and have the ability to learn and generalize. The neuron is the information processing unit of a neural network and the basis for designing numerous neural networks. The most fundamental network architecture is a single-layer neural network, where the single-layer refers to the output layer of computation neurons. This chapter introduces Rosenblatt's neuron. Rosenblatt's perceptron occupies a special place in the historical development of neural networks. The chapter also considers the performance of the perceptron network and is in a position to introduce the perceptron learning rule. This learning rule is an example of supervised training, in which the learning rule is provided with a set of examples of proper network behavior. Finally the chapter further discusses activation function and its types, including a threshold function, or Heaviside function and sigmoid function.

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