04440nam a22003257a 4500 OSt 20260803093712.0 260731b |||||||| |||| 00| 0 eng d 9781916784277 [hardbound] University of Cebu-Banilad University of Cebu-Banilad Mills, Bonnie, author. Fundamentals of artificial neural network and fuzzy logic / by Bonnie Mills. First edition. London, United Kingdom : Vintage Press Ltd., c2024. x, 264 pages : color illustrations ; 24 cm. rdacontent text rdamedia unmediated rdacarrier volume Includes bibliographical references and index. Contents: 1. Introduction -- 2. Concepts in artificial neural networks -- 3. Biological neural networks -- 4. Neural programme paradigms -- 5. Fuzzy logic -- 6. Fuzzy logic networks. "Neural network is an information processing system that is inspired by the way biological nervous systems such as brain process information. A neural network is composed of a large number of interconnected processing elements known as neurons which are used to solve problems. A neural network is an attempt to make a computer model of the human brain and neural networks are parallel computing devices. A neural network can refer to either a neural circuit of biological neurons (sometimes also called a biological neural network), or a network of artificial neurons or nodes in the case of an artificial neural network. Artificial neural networks are used for solving artificial intelligence (AI) problems; they model connections of biological neurons as weights between nodes. A positive weight reflects an excitatory connection, while negative values mean inhibitory connections. All inputs are modified by a weight and summed. This activity is referred to as a linear combination. Finally, an activation function controls the amplitude of the output. For example, an acceptable range of output is usually between 0 and 1, or it could be 1 and 1. These artificial networks may be used for predictive modeling, adaptive control and applications where they can be trained via a dataset. Self-learning resulting from experience can occur within networks, which can derive conclusions from a complex and seemingly unrelated set of information. The utility of artificial neural network models lies in the fact that they can be used to infer a function from observations and also to use it. Unsupervised neural networks can also be used to learn representations of the input that capture the salient characteristics of the input distribution, e.g., see the Boltzmann machine (1983), and more recently, deep learning algorithms, which can implicitly learn the distribution function of the observed data. Learning in neural networks is particularly useful in applications where the complexity of the data or task makes the design of such functions by hand impractical. Neural networks can be used in different fields. The term fuzzy represents the things which are not clear. In the real world many times we find a situation where we can't determine whether the state is true or false, their fuzzy logic provides very valuable flexibility for reasoning. In this way, we can consider the inaccuracies and uncertainties of any situation. In a narrow sense, fuzzy logic is a logical system, which is an extension of multivalued logic. However, in a wider sense fuzzy logic (FL) is almost synonymous with the theory of fuzzy sets, a theory which relates to classes of objects without crisp, clearly defined boundaries. This comprehensive book offers a simple presentation and bottom-up approach that is ideal for working professional engineers, undergraduates, medical/biology majors, and anyone with a non-specialist background. Readers will understand the fundamentals of the emerging field of fuzzy neural networks, their applications and the most used paradigms with this carefully organized state-of-the-art textbook —Bonnie Mills." --Preface Adult Ortega, Eric College of Computer Studies Computer Science Neural networks (Computer science). Fuzzy logic. Artificial intelligence. ddc BK Janna [new] 07/30/2026 15573 15573 0 0 ddc 0 0 UCBL_MAIN UCBL_MAIN SUBJ.REF 2026-07-31 Albasa-Mindmover 8250.00 0 006.32 M62 2024 3UCBL000029696 2026-07-31 2026-07-31 SR