Intelligent Systems is developing the first self-contained volume that includes the new concept was established as a whole, from a systematic methodology for case studies for industrial applications. Explain the following basic approach is to develop intelligent system developed (EIS): the Hierarchical Prioritized Structure Participatory Learning Paradigm the Takagi-Sugeno fuzzy Developing the system (ETS +) clustering algorithm developed from well-known offline Gustafson-Kessel clustering algorithm Emphasizing the importance and increasing interest in online processing of data streams Outlines the general strategy of using dynamic fuzzy clustering as a basis for evolvable information granulation Presents a methodology for developing robust and interpretable fuzzy rule-based system developed Introducing an integrated approach for the additional (real-time) feature extraction and classification Propose a study of the stability of developing recurrent neuro-fuzzy network Details methodology for developing clustering and classification Reveals a variety of applications of EIS to address the real problems in the field: developing sensors can be concluded in the chemical and petrochemical industry learning and recognition in robotics Recommended resources downloadable software Intelligent Systems is developing a one-stop reference guide for both theoretical and practical problems for computer scientists, engineers, researchers, applied mathematicians, machine learning and data mining experts, graduate students, and professionals.