The purpose of this volume is to provide a brief review of the previous work on model reduction and identifi cation of distributed parameter systems (DPS), and develop new spatio-temporal models and their relevant identifi cation approaches.
In this book, a systematic overview and classifi cation on the modeling of DPS is presented fi rst, which includes model reduction, parameter estimation and system identifi cation. Next, a class of block-oriented nonlinear systems in traditional lumped parameter systems (LPS) is extended to DPS, which results in the spatio-temporal Wiener and Hammerstein systems and their identifi cation methods. Then, the traditional Volterra model is extended to DPS, which results in the spatio-temporal Volterra model and its identification algorithm. All these methods are based on linear time/space separation. Sometimes, the nonlinear time/space separation can play a better role in modeling of very complex processes.
Thus, a nonlinear time/space separation based neural modeling is also presented for a class of DPS with more complicated dynamics. Finally, all these modeling approaches are successfully applied to industrial thermal processes, including a catalytic rod, a packed-bed reactor and a snap curing oven. The work is presented giving a unifi ed view from time/space separation. The book also illustrates applications to thermal processes in the electronics packaging and chemical industry. This volume assumes a basic knowledge about distributed parameter systems, system modeling and identifi cation. It is intended for researchers, graduate students and engineers interested in distributed parameter systems, nonlinear systems, and process modeling and control.
Within all large consumer facing organizations, most decisions about how to deal with people are made automatically by computerized decision making systems. Information about people, their lifestyle and past behavior are used to predict how they are expected to behave in the future. It can be determined if someone applying for a bank loan will make their repayments, who will respond to a marketing communication and the likelihood that someone will claim on their insurance policy. This book provides a step-by-step guide to how Predictive Analytics is used by some of the world's most influential organizations. This includes international banks, leading insurance providers, credit reference agencies and national governments. It covers all stages of the Predictive Analytics process. This includes project management, data collection, sampling, data transformation and pre-processing, model construction, validation, implementation and post-implementation monitoring of the model's performance.
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