Soft Computing for Hybrid Intelligent Systems by Ricardo Martínez, Oscar Castillo, Luis T. Aguilar (auth.),
By Ricardo Martínez, Oscar Castillo, Luis T. Aguilar (auth.), Oscar Castillo, Patricia Melin, Janusz Kacprzyk, Witold Pedrycz (eds.)
Soft Computing (SC) includes a number of clever computing paradigms, together with fuzzy good judgment, neural networks, and evolutionary algorithms, that are used to supply strong hybrid clever platforms. This edited booklet includes papers on assorted facets of sentimental computing and hybrid clever structures. There are theoretical facets in addition to software papers. New tools and purposes of hybrid clever structures utilizing gentle computing ideas are defined. The ebook is prepared in 5 major components, which comprise a gaggle of papers round an identical topic. the 1st half involves papers with the most subject matter of clever regulate, that are essentially papers that use hybrid structures to unravel specific difficulties of regulate. the second one half includes papers with the most topic of development reputation, that are essentially papers utilizing gentle computing recommendations for reaching trend acceptance in several functions. The 3rd half includes papers with the subjects of clever brokers and social structures, that are papers that follow the guidelines of brokers and social habit to resolve real-world difficulties. The fourth half comprises papers that care for the implementation of clever platforms for fixing specific difficulties. The 5th half includes papers that take care of modeling, simulation and optimization for real-world applications.
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Water tank system submask 3 Type-1 Fuzzy Inference Systems The human brain interprets imprecise and incomplete sensory information provided by perceptive organs. Fuzzy set theory provides a systematic calculus to deal with such information linguistically, and it performs numerical computation by using linguistic labels stipulated by membership functions. Moreover, a selection of fuzzy If-Then rules forms the key component of a fuzzy inference system (FIS) that can effectively model human expertise in a specific application .
8 reveals the gear backlash effect. 2 rad. The stiffness coefficient is of K=5 Nm/rad. Table 2 represents the parameters of the motor, taken from the manufacturer data specifications, and the nominal load parameters, and the load parameters, are taken from . Fig. 7. Experimental test bench Fig. 8. Backlash hysteresis before compensation Fuzzy Control for Output Regulation of a Servomechanism with Backlash 27 Table 2. 2 Simulation Results The experiments were carried out for the closed-loop system, and we consider the angular motor position as the only information available for feedback.
Nonlinear -Output Regulation of a Nonminimum Phase Servomechanism With Backlash. : A Fuzzy System Compensator for Backlash. In: Procc. Of the 1998 IEEE Int. Conf. on Robotics & Automation, Leuven, Belgium, pp. : High-Precision Position of a Mechanism with Nonlinar Friction Using a Fuzzy Logic Pulse Controller. IEEE Trans. : Adaptive Control of Robot Manipulator Using Fuzzy Compensator. IEEE Trans. : New Models and Identification Methods for Backlash and Gear Play. , Lewis, F. ) Adaptive Control of Nonsmooth Dynamic Systems, pp.