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This paper proposes a methodology for the design of fuzzy inference systems based on Boolean relations. The approach using Boolean sets presents limited performance due to the abrupt transitions that occur during its functioning, therefore, fuzzy sets can be used aiming the improvement of the performance. In this approach, firstly, the design of a Boolean controller is performed, which is later extended into fuzzy under design guidelines proposed in this paper. The methodology uses Kleene algebra via truth tables for the fuzzy system design, allowing the simplification of the equations that implement the fuzzy system.

The spectral handoff is important in cognitive wireless networks to ensure an adequate quality of service and performance for secondary user communications. This work presents a multivariable algorithm for dynamic channel selection used in cognitive wireless networks. The channel selection is based on the fuzzy analytical hierarchical process (FAHP) method. The selected criteria for choosing the best backup channel are probability of channel availability, estimated channel time availability, signal to noise plus interference ratio, and bandwidth. These criteria are determined by means of a customized Delphi Method and using the FAHP technique; the corresponding weight and significance is calculated for two applications classified as best effort (BE) and real time (RT). The insertion of the fuzzy logic in the AHP algorithm allows better handling of inaccurate information because, as shown the results, consider more options to evaluate in contrast to a conventional AHP. As a difference with related work, the performance of our proposed FAHP method was validated with captured data in experiments realized at the GSM frequency band (824-849 MHz). This is due to the challenge of finding white spaces to communicate in this frequency band. This band represents more disputes in accessing spectral opportunities than other radio frequency (RF) bands because of the high demand for mobile phone communications. The proposed FAHP algorithm has a practical computational complexity and provides an effective frequency-channel selection. This proposed FAHP algorithm presents a new methodology to select and classify the variables based on a modified version of the Delphi method. The results of the proposed method were contrasted numerically with other three methods.

This document presents some considerations and procedures to design a compact fuzzy system based on Boolean relations. In the design process, a Boolean codification of two elements is extended to a Kleene’s of three elements to perform simplifications for obtaining a compact fuzzy system. The design methodology employed a set of considerations producing equivalent expressions when using Boole and Kleene algebras establishing cases where simplification can be carried out, thus obtaining compact forms. In addition, the development of two compact fuzzy systems based on Boolean relations is shown, presenting its application for the identification of a nonlinear plant and the control of a hydraulic system where it can be seen that compact structures describes satisfactory performance for both identification and control when using algorithms for optimizing the parameters of the compact fuzzy systems. Finally, the applications where compact fuzzy systems are based on Boolean relationships are discussed allowing the observation of other scenarios where these structures can be used.

A laboratory-scale Activated Sludge System (ASS) was employed for the biodegradation of coke wastewater, which contains high concentrations of ammonium, thiocyanate, phenols and other organic compounds. The well-known kinetics models of Monod or Haldane are not very useful due to inhibition phenomena amongst the pollutants and also, they need the determination of a wide range of parameters to be introduced in the models. In this paper, a feed-forward neural network is outlined to obtain a satisfactory approach for estimating the effluent ammonium concentration of the treatment plant. The methodology consists in performing a group of different sizes of the hidden layer and different subsets of input variables.The developed model is useful to obtain simulations under different conditions of the influent stream, thus enabling the effluent ammonium concentration to be estimated. This neural network achieves better results than classical mathematical models for biological wastewater treatment as a result of the complex composition of the coke wastewater.

Systems of distributed generation have shown to be a remarkable alternative to a rational use of energy. Nevertheless, the proper functioning of them still manifests a range of challenges, including both the adequate energy dispatch depending on the variability of consumption and the interaction between generators. This paper describes the implementation of an adaptive neurofuzzy system for voltage control, regarding the changes observed in the consumption within the distribution system. The proposed design employs two neurofuzzy systems, one for the plant dynamics identification and the other for control purposes. This focus optimizes the controller using the model achieved through the identification of the plant, whose changes are produced by charge variation; consequently, this process is adaptively performed. The results show the performance of the adaptive neurofuzzy system via statistical analysis.

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