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Fuzzy cellular models (FCM) are a combination of cellular automata (CA) and fuzzy logic (FL). FCM are used to simulate complex dynamics ecological systems that involve space and time. The goal of this paper was to define a population growth model as a transition function that determines the state of each cell. The transition function involves FL to model variability of mortality, reproduction and...
In this paper a genetic-type-2 fuzzy approach is proposed to optimize the parameters of the membership functions (MFs) of a type-2 fuzzy logic system (FLS) applied to control, we design a chromosome to represent the parameters of the MFs of a preestablished type-2 FLS, design a fitness function and select genetic operators. A case of study is proposed to evaluate the optimization process, this is...
This paper presents the development and design of two software tools for computational intelligence. The software tools include a graphical user interface for construction, edition and observation of the intelligent systems. The software tool are for interval type-2 fuzzy logic and modular neural networks. The interval type-2 fuzzy logic system toolbox (IT2FLS), is an environment for interval type-2...
We describe in this paper a comparative study of fuzzy inference systems as methods of integration in modular neural networks (MNNpsilas) for multimodal biometry. These methods of integration are based on type-1 and type-2 fuzzy logic. Also, the fuzzy systems are optimized with simple genetic algorithms. First, we considered the use of type-1 fuzzy logic and later the approach with type-2 fuzzy logic...
This work focuses in the design and implementation of a digital fuzzy controller, that uses a novel tuning technique called simple tuning algorithm (STA) to achieve the desired controllerpsilas response. Improvements over exciting architectures to implement the fuzzy inference systems (FIS) in a field programmable gate array (FPGA) are presented. A methodology that minimizes the test and validation...
Cognitive map and fuzzy logic controller hybrid model is presented in this paper. Sample control applications are included to demonstrate incorporation of analytical and empirical knowledge on the fuzzy cognitive map construction with the purpose of generating fuzzy logic controller (FLC) design on-line. Cognitive map state vector includes all FLC-defining concepts. Controller performance concepts...
We describe a tracking controller for the dynamic model of a unicycle mobile robot by integrating a kinematic and a torque controller based on type-2 fuzzy logic theory and genetic algorithms. Computer simulations are presented confirming the performance of the tracking controller and its application to different navigation problems.
We describe in this paper a new hybrid approach for mathematical function optimization combining particle swarm optimization (PSO) and genetic algorithms (GAs) using fuzzy logic to integrate the results. The new evolutionary method combines the advantages of PSO and GA to give us an improved PSO+GA hybrid method. Fuzzy logic is used to combine the results of the PSO and GA in the best way possible...
In this paper, a class of Interval Type-2 Fuzzy Neural Networks (IT2FNN) is proposed, which is functionally equivalent to interval type-2 fuzzy inference systems. The computational process envisioned for fuzzy neural systems is as follows: it starts with the development of an rdquoInterval Type-2 Fuzzy Neuronrdquo, which is based on biological neural morphologies, followed by the learning mechanisms...
In the paper it is proposed a new recurrent fuzzy-neural multi-model (FNMM) identifier applied for decentralized identification of a distributed parameter anaerobic wastewater treatment digestion bioprocess, carried out in a fixed bed and a recirculation tank. The distributed parameter analytical model of the digestion bioprocess is reduced to a lumped system using the orthogonal collocation method,...
It is presented a flexible architecture that allows to implement an embedded nonlinear fuzzy controller into an FPGA which can be easily tuned through the use of the Simple Tuning Algorithm (STA) without a controller reference model. The model was developed using VHDL programming, and it was tested in soft real time using Xilinx System Generator and Simulink before the final implementation into the...
A hybrid model of cognitive map and Mamdani type fuzzy logic controller (CM-FLC) is presented; cognitive map portion of the system generates FLC design online using data from empirical sources, it also uses measured variable data to characterized controller performance in order to generate FLC fine-tuning, producing adaptive control behavior. Model sensitivity to designer specified control objectives...
Air pollution is a growing problem arising from domestic heating, high density of vehicle traffic, electricity production, and expanding commercial and industrial activities, all increasing in parallel with urban population. Monitoring and forecasting of air quality index in urban areas is important due to health impact. Hybrid intelligent techniques are successfully used in modeling of highly complex...
This paper describes reactive control of a mobile robot using fuzzy logic in a distributed environment. Simulation results of the reactive fuzzy controller in a particular maze problem illustrate the effectiveness of the proposed approach. The mobile robot is able to solve the maze problem with the use of fuzzy rules designed with expert knowledge.
This paper describes a comparison of results in breast cancer diagnosis, using two fuzzy logic methods; the first case uses fuzzy clustering with the fuzzy c-means (FCM) algorithm, which tries to find similarities between different variables; the second method is an implementation of a fuzzy inference system (FIS) with a genetic algorithm (GA) for creating and activating the optimal rules.
This work presents the implementation of a wireless system for the control of mobile robots using circuits with Self-Timed (ST) Synchronization, implemented in reconfigurable devices FPGAs. The system is composed of a global network of small ST processors, which will develop independent processes communicated by means of modules of wireless transmission that form the network of activation of peripheral...
We describe in this paper a comparative study between Fuzzy Inference Systems as methods of integration in modular neural networks for multimodal biometry. These methods of integration are based on techniques of type-1 fuzzy logic and type-2 fuzzy logic. Also, the fuzzy systems are optimized with simple genetic algorithms. First, we considered the use of type-1 fuzzy logic and later the approach with...
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