Industrial Strength Empirical Modeling clearly explains the mainprinciples of the different machine learning approaches and offersa methodology of how to integrate these techniques for successfulreal world implementation. It covers the conceptualfoundations of selected machine learning approaches liketraditional neural networks, analytical neural networks, supportvector machines, and particle swarm optimizers at a moderatetheoretical depth. Once this foundation is established it provides a frameworkfor successful industrial application of these soft computingtechniques including issues of data integrity, sensitivityanalysis, model deployment and maintenance, value assessment, andempirical modeling project management. Likewise, early in thebook, simple examples illustrate the fundamental concepts;later, case studies are introduced to better addressthe complex nature of real–worldapplications. Throughout the text, the authors provide "mindmaps" to help readers visualize and consolidate concepts.