Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks. Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computation time of the training process is critical, a fast learning complex-valued neural network called as a fully complex-valued relaxation network along with its learning algorithm has been presented. The presence of orthogonal decision boundaries helps complex-valued neural networks to outperform real-valued networks in performing classification tasks. This aspect has been highlighted. The performances of various complex-valued neural networks are evaluated on a set of benchmark and real-world function approximation and real-valued classification problems.
This textbook provides a general overview of porous media flow, and introduces various theoretical tools to characterize and predict the flow. It has been written for graduate and advanced graduate students in various engineering disciplines. It includes the topics such as fluid flow, conduction, convection, and radiation in porous media as well as porous medium aspects of biological systems. The concepts are supported by numerous solved examples to aid self-learning in students. The textbook also contains illustrated diagrams for better understanding of the concepts. This textbook will be useful for the core course of "Flow through Porous media" for graduate and advanced graduate students in various engineering disciplines. This textbook will also serve as a refresher course for researchers who are engaged in research related to porous media flow.
Digitization and Artificial Intelligence are at the center of every board room conversation these days. Most CEOs, senior management and boards are less worried about their traditional competitors. The impact of disruption through digitization is real and quantifiable – 52% of Fortune 500 companies have been replaced since 2000. The task of enabling new digital business models gets exponentially harder as the complexity of systems are greater. Most CIOs, CTOs are struggling with when to start, what to do, and how to meet the expectations of their CEOs and Boards. Design patterns help narrow this gap by documenting a well-working solution to a problem that occurs repeatedly in a given context. “Enterprise Digitization Patterns” breaks down digital disruption enablers and delivers a cookbook across three key pillars – Digital Experience, Enterprise IoT and Autonomous Systems. The book provides reference architectures, design patterns, maturity models and practical case studies to drive new forms of customer value, business outcomes and business models. The design patterns are distinct or relevant to modern-day enterprise digital platforms that enables enterprise digital business models.
Recent advancements in the field of telecommunications, medical imaging and signal processing deal with signals that are inherently time varying, nonlinear and complex-valued. The time varying, nonlinear characteristics of these signals can be effectively analyzed using artificial neural networks. Furthermore, to efficiently preserve the physical characteristics of these complex-valued signals, it is important to develop complex-valued neural networks and derive their learning algorithms to represent these signals at every step of the learning process. This monograph comprises a collection of new supervised learning algorithms along with novel architectures for complex-valued neural networks. The concepts of meta-cognition equipped with a self-regulated learning have been known to be the best human learning strategy. In this monograph, the principles of meta-cognition have been introduced for complex-valued neural networks in both the batch and sequential learning modes. For applications where the computation time of the training process is critical, a fast learning complex-valued neural network called as a fully complex-valued relaxation network along with its learning algorithm has been presented. The presence of orthogonal decision boundaries helps complex-valued neural networks to outperform real-valued networks in performing classification tasks. This aspect has been highlighted. The performances of various complex-valued neural networks are evaluated on a set of benchmark and real-world function approximation and real-valued classification problems.
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