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"Neural networks and fuzzy systems are model free control design approaches that represent an advantage over classical control when dealing with complicated nonlinear actuator dynamics. Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities brings neural networks and fuzzy logic together with dynamical control systems. Each chapter presents powerful control approaches for the design of intelligent controllers to compensate for actuator nonlinearities such as time delay, friction, deadzone, and backlash that can be found in all industrial motion systems, plus a thorough development, rigorous stability proofs, and simulation examples for each design. In the final chapter, the authors develop a framework to implement intelligent control schemes on actual systems. Rigorous stability proofs are further verified by computer simulations, and appendices contain the computer code needed to build intelligent controllers for real-time applications. Neural networks capture the parallel processing and learning capabilities of biological nervous systems, and fuzzy logic captures the decision-making capabilities of human linguistics and cognitive systems."

"This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural networks with the aim of designing intelligent systems for complex pattern recognition problems, including iris, ear, face and voice recognition. The third part contains chapters with the theme of evolutionary optimization of type-2 fuzzy systems and modular neural networks in the area of intelligent pattern recognition, which includes the application of genetic algorithms for obtaining optimal type-2 fuzzy integration systems and ideal neural network architectures for solving problems in this area."

"Pada masa sekarang ini perkembangan teknologi cenderung memiliki kemampuan untuk berpikir dan mengambil keputusan layaknya manusia. Salah satu dari banyak metode untuk mengembangkan teknologi yang cerdas adalah dengan menggunakan Adaptive Neuro Fuzyy Inference System. Penelitian ini dilakukan dengan menerapkan ANFIS tipe Sugeno pada data-data penelitian umum, seperti data tanaman iris dan data ionosphere, melihat efek perubahan parameter-parameter terhadap recognisinya, lalu melakukan ANFIS terhadap data citra wajah.

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The technology nowadays tends to have abbility to think and to size up decision, just like us humans. One of the kind of method to enhance smart technology is by using Adaptive Neuro Fuzyy Inference System. This research is done by using ANFIS Sugeno type on general research data, such as iris plant data and ionosphere data, observing the effect of the changing parameter over the recognition, then using ANFIS on face image data."