Evolutionary Design of Intelligent Systems in Modeling, by Ricardo Martínez-Marroquín, Oscar Castillo, José Soria

By Ricardo Martínez-Marroquín, Oscar Castillo, José Soria (auth.), Oscar Castillo, Witold Pedrycz, Janusz Kacprzyk (eds.)

The editors describe during this ebook, new equipment for evolutionary layout of clever platforms utilizing delicate computing and their purposes in modeling, simulation and keep watch over. gentle Computing (SC) contains numerous clever computing paradigms, together with fuzzy good judgment, neural networks, and evolutionary algorithms, which are used to provide strong hybrid clever structures. The ebook is prepared in 4 major components, which include a gaggle of papers round an identical topic. the 1st half involves papers with the most subject matter of evolutionary layout of fuzzy platforms in clever keep an eye on, which is composed of papers that suggest new equipment for designing and optimizing clever controllers for various functions. the second one half comprises papers with the most subject matter of evolutionary layout of clever structures for trend reputation functions, that are primarily papers utilizing evolutionary algorithms for optimizing modular neural networks with fuzzy platforms for reaction integration, for attaining trend acceptance in numerous purposes. The 3rd half comprises papers with the topics of types for studying and social simulation, that are papers that observe clever platforms to the issues of designing studying gadgets and social brokers. The fourth half includes papers that take care of clever platforms in robotics purposes and implementations.

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1671–1676 (2002) 18. , Mendes: The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002) 19. : Position Control for Wheeled Mobile Robot Using a Fuzzy Controller, pp. 525–528. IEEE, Los Alamitos (1999) 20. : Interval Type-2 Fuzzy Logic Systems: Theory and Design. IEEE Trans. on Fuzzy Systems 8(5), 535–550 (2000) 21. : Type-2 fuzzy logic systems. IEEE Trans. Fuzzy Systems 7, 643–658 (1999) 22. : Type-2 Fuzzy Sets Made Simple.

We can describe the proposed method as follows: In the operation of the model, we have 2 main blocks, which are responsible for knowledge and learning (with the paradigm of intelligent agents) and the vision and control (using fuzzy logic). These modules are described below, the first module contains 3 agents, NA [Node Agent], TA [Task Agent] SA [System Agent] Agent System (AS), and will know every time the operation of the other agents. To detect a change in the environment the Agent Node [that is in charge of the sensors (ultrasonic, camera and two light sensors)] with the ultrasonic sensor and two light sensors, starts its operation, and we start at the state called reactive control; this because you have to move and sense all the time until it finds an obstacle, this can happen once a photo is taken, which will be sent to the database of images (BDI), the image will be applied a pre-processing for recognition in order to know whether the object is found, this decision was taken on the angle (angle to take the decision to bypass the object), able to escape and move forward, trend data, they are caught in the trajectory control process, to observe the speed values, the Task Agent (which is responsible for the drive), once all the parameters necessary for the performance of the robot agent system (AS) who is the coordinator, and executes instructions in the robot.

12. Structure of the fuzzy system that works for the simulations. The fuzzy system has three inputs, two outputs and consists of ten rules for inference. The first input variable of the fuzzy system is the ultrasonic sensor, which has three membership functions, which are linguistic (close, near, far), as shown in Fig. 13. Fig. 13. Membership functions for the ultrasonic sensor. The second variable of the fuzzy system is the light sensor that has two membership functions that are free and wall, as shown in Fig.

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