Automatic Generation Of Fuzzy Rules Research Articles

Application of a novel dynamic recurrent fuzzy neural network with rule self-adaptation based on chaotic quantum pigeon-inspired optimization in modeling for gas turbine

With the rapid development of renewable energy, higher requirements are put forward for the control ability of thermal power units. And an accurate system model is the premise and guarantee of controller design. In this article, a novel dynamic recurrent fuzzy neural network with rule self-adaptation modeling approach based on chaotic quantum pigeon-inspired optimization (NDRFNN-CQPIO) is proposed for the modeling of gas turbine system. The introduction of recursive layer, automatic generation of fuzzy rules and fast parameter solving are the main features of the proposed algorithm. First, set the appropriate threshold, and process the system error and distance index through 4 preset cases to generate fuzzy rules. When a new rule is generated, its antecedent parameters are determined at the same time, and define a boundary to determine whether the antecedent parameters need to be adjusted, so as to avoid the generation of redundant Gaussian functions. Then, a pruning technique based on the contributions of the normalized neuron and learning error is raised to trim the redundant fuzzy rules to ensure the model structure more compact and efficient. Finally, using the chaotic quantum pigeon-inspired algorithm to solve the consequent parameters quickly and accurately. The actual operation data of a gas turbine system were selected to identify and verify the model. In the established model, the root mean square error of output power, exhaust gas temperature, rotor speed, and exhaust gas flow are 0.0023, 0.0015, 0.0011 and 0.0069, respectively. The simulation results show that the proposed approach is effective and feasible, and it can be used for the model identification of gas turbine system. Moreover, we conducted robustness analysis of the model and proved that the proposed model has strong robustness, which provides a basis for its further application.

Adaptive HVAC System Based on Fuzzy Controller Approach

Heating, ventilation, and air conditioning (HVAC) system performance research has received much attention in recent years. Many researchers suggest a set of appropriate fuzzy inputs that can be used to design fuzzy rules-based smart thermostats or controllers that can respond to demand-controlled ventilation, which in turn optimizes HVAC energy usage and provides satisfactory indoor temperatures. Previous research has focused on limited input parameters, such as indoor occupancy status, ambient temperature, and humidity constraints, which cannot efficiently and precisely manage thermal comfort. Hence, this study proposes a novel fuzzy controller with additional input parameters to keep indoor thermal comfort consistent with the corresponding number of occupants. The process employs an automatic fuzzy rule generation method to simplify the task of generating rules in the fuzzy inference system (FIS) using Mamdani FIS. A design-builder is used for modeling the HVAC systems. Local weather data were used to conduct simulations via EnergyPlus. The thermal comfort analysis using the Fanger model for three different scenarios shows that the proposed FIS controller can successfully respond to the indoor comfort variation in all possible scenarios and ensure a satisfactory comfort level. The proposed method demonstrates up to 50% energy savings if occupants do not worry about comfort.

Automatic generation of fuzzy inference systems for multivariate time series forecasting

For the time series forecasting process, the model must learn the variables' temporal dynamics to obtain consistent future values. Although its forecast may depend only on past values in some time series, there are numerous applications in which multivariate forecasting is necessary for a more accurate result. Therefore a model that deals with this type of problem is needed. In addition, explainability is an essential feature for choosing the forecast model since it is possible to extract insights and understand which variables were most important for the result generated. A Fuzzy Inference System, named AutoMFIS, is a model for multivariate time series forecasting with good accuracy and interpretability. However, despite the favorable characteristics, this system has limitations that make this technique not practical in problems with high-dimensionality databases. This article proposes an extension to the base model AutoMFIS for time series forecasting with high dimensionality data, named e-AutoMFIS. Based on the Ensemble learning theory, this new methodology applies distributed learning for automatic fuzzy rules generation and rule filtering to keep the system semantically interpretable. The proposed model is evaluated in different case studies, in which the results are compared in terms of accuracy along with other methods in the literature. In addition, in each selected problem, the interpretability aspect is also assessed.

Improvement of the energy production of a photovoltaic-wind hybrid system using NF-PSO MPPT

This manuscript gives a contribution to the optimization of a hybrid Photovoltaic-Wind Turbine system with a storage system. In order to capture the maximum power that can be produced by each source, while maintaining the rotor speed of the wind turbine at its maximum values according to wind variations, the Neuro-Fuzzy-Particle Swarm Optimization (NF-PSO) controller is proposed. The Neuro-Fuzzy method is used here because it allows an automatic generation of fuzzy rules, and the Particle Swarm Optimization to find an optimal gain allowing to readjust the dynamics of the fuzzy rules by reducing the power losses (oscillations). For the proper functioning of such a system, we have developed a fuzzy supervisor in order to have an optimal control of the system according to the variations of the requested load and the produced power by considering the storage system and the load shedding. The simulation results of the system confirmed the better performance of this method in terms of speed with a response time of 0.2s on the wind side and 0.025s on the side photovoltaic, of efficiency with 99.87% on the photovoltaic side and 99.6% on the wind side, and above all in term of oscillation reduction with practically a negligible oscillation rate compared to the NF and the Cuckoo algorithm.

Automatic Generation of Fuzzy Classification Rules from Data

In this paper, we propose a method for automatic generation of fuzzy rules for data classification. The proposed method is based on subtractive clustering optimized using genetic algorithm. It searches for the FIS structure and number of rules that have the highest fitness value. Multiple performance measures are incorporated into the fitness function to address the problem of imbalanced data. Fitness function includes both training and validation to avoid data over-fitting. Classification performance of the proposed method is evaluated using different data sets and results are compared to those of a number of models generated by fuzzy cmeans clustering with various cluster numbers. Results show that the proposed method has better accuracy and a well compromised sensitivity and specificity.

Fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions

PurposeThis paper investigates the suitability of fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.Design/methodology/approachTwo fuzzy-logic-based support tools are developed together with experts from a Swedish manufacturing firm. The first uses a complete rule base and the second a reduced rule base. Sixteen inference settings are used in both of the support tools.FindingsThe findings show that fuzzy-logic-based support tools are suitable for initial screening of manufacturing reshoring decisions. The developed support tools are capable of suggesting whether a reshoring decision should be further evaluated or not, based on six primary competitiveness criteria. In contrast to existing literature this research shows that it does not matter whether a complete or reduced rule base is used when it comes to accuracy. The developed support tools perform similarly with no statistically significant differences. However, since the interpretability is much higher when a reduced rule base is used and it require fewer resources to develop, the second tool is more preferable for initial screening purposes.Research limitations/implicationsThe developed support tools are implemented at a primary-criteria level and to make them more applicable, they should also include the sub-criteria level. The support tools should also be expanded to not only consider competitiveness criteria, but also other criteria related to availability of resources and strategic orientation of the firm. This requires further research with regard to multi-stage architecture and automatic generation of fuzzy rules in the manufacturing reshoring domain.Practical implicationsThe support tools help managers to invest their scarce time on the most promising reshoring projects and to make timely and resilient decisions by taking a holistic perspective on competitiveness. Practitioners are advised to choose the type of support tool based on the available data.Originality/valueThere is a general lack of decision support tools in the manufacturing reshoring domain. This paper addresses the gap by developing fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.

Автоматическая генерация базы нечетких правил на основе числовых данных для управления мобильным роботом с гусеничным шасси

Автоматическая генерация базы нечетких правил на основе числовых данных для управления мобильным роботом с гусеничным шасси

Optimized Automatic Generation of Fuzzy Rules for Nonlinear System Based on Subtractive Clustering Algorithm for Medical Image Segmentation

Optimized Automatic Generation of Fuzzy Rules for Nonlinear System Based on Subtractive Clustering Algorithm for Medical Image Segmentation

Hybrid Fuzzy Adaptive Wiener Filtering with Optimization for Intrusion Detection

Intrusion detection plays a key role in detecting attacks over networks, and due to the increasing usage of Internet services, several security threats arise. Though an intrusion detection system (IDS) detects attacks efficiently, it also generates a large number of false alerts, which makes it difficult for a system administrator to identify attacks. This paper proposes automatic fuzzy rule generation combined with a Wiener filter to identify attacks. Further, to optimize the results, simplified swarm optimization is used. After training a large dataset, various fuzzy rules are generated automatically for testing, and a Wiener filter is used to filter out attacks that act as noisy data, which improves the accuracy of the detection. By combining automatic fuzzy rule generation with a Wiener filter, an IDS can handle intrusion detection more efficiently. Experimental results, which are based on collected live network data, are discussed and show that the proposed method provides a competitively high detection rate and a reduced false alarm rate in comparison with other existing machine learning techniques.

Automatic Generation of Fuzzy Rules for the Control of a Mobile Robot

This paper is aimed at looking into the generation of a fuzzy rule base in order to control a mobile robot in a navigation space. The procedure is based on a partitioning of the state space into subsets called cell groups. Membership functions are assigned for the state and controls. The transition from one conditional subspace to another is accomplished via a center point mapping of the cell groups, under the applied action of each IF-THEN rule. Two methods generating fuzzy rules are used.First, a manual method generating the rule base, in the case of rough quantification of variables, is proposed. This proposition is very complex and nonapplicable in the case of fine qnantification.Next, we used an automatic method for generating the rule base: (i) the way transitional elements are clustered on the basis of an explicitly defined performance measure, (ii) optimum transitions are selected using the dynamic programming procedure, (iii) the fuzzy rules are generated automatically for transitioning from any initial cell to the target cell. Three improvements are proposed for the automatic method: the use of a fine quantification, the use of the polar coordinates, and the use of a first-order Sugeno fuzzy controller.

A fast approach for automatic generation of fuzzy rules by generalized dynamic fuzzy neural networks

A fast approach for automatically generating fuzzy rules from sample patterns using generalized dynamic fuzzy neural networks (GD-FNNs) is presented. The GD-FNN is built based on ellipsoidal basis functions and functionally is equivalent to a Takagi-Sugeno-Kang fuzzy system. The salient characteristics of the GD-FNN are: (1) structure identification and parameters estimation are performed automatically and simultaneously without partitioning input space and selecting initial parameters a priori; (2) fuzzy rules can be recruited or deleted dynamically; (3) fuzzy rules can be generated quickly without resorting to the backpropagation (BP) iteration learning, a common approach adopted by many existing methods. The GD-FNN is employed in a wide range of applications ranging from static function approximation and nonlinear system identification to time-varying drug delivery system and multilink robot control. Simulation results demonstrate that a compact and high-performance fuzzy rule-base can be constructed. Comprehensive comparisons with other latest approaches show that the proposed approach is superior in terms of learning efficiency and performance.

Closed-loop fault diagnosis based on a nonlinear process model and automatic fuzzy rule generation

In this contribution a new approach for fault detection and diagnosis (FDD) for nonlinear processes is presented. A nonlinear fuzzy model with transparent inner structure is used for the generation of relevant symptoms. The resulting symptom patterns are classified with a new self-learning classification structure based on fuzzy rules. The approach is successfully applied to an electro-pneumatic valve in a closed control loop.

Self-testing and self-learning fuzzy expert system for technological process control

The application of expert systems in technological processes control is usually limited. One problem is how to respond to changes in the environment surrounding system. A way to overcome the above shortcoming is to use fuzzy logic for representation of the inexact information. Still more the fuzzy system could be responsible for the changes in the environment if it is self-learning by automatic fuzzy rules generation. Another problem is how to balance the conflicting situations concerning facts and rules. The certainty factor formalism in classical expert systems is rather subjective assessment to overcome this. In the present work a self-testing method is proposed, based on fuzzy certainty factor. The system is designed to find the contradictory facts and incompleteness of the fuzzy rule-base. A self-learning and self-testing fuzzy expert system is presented, where the control actions of a complex technological process could be automatically generated. The system can operate in two modes, expert and fuzzy ones, that corresponds to implicit and explicit use of human knowledge during the control process. The fuzzy rule base can be automatically tested for completeness and correctness using production rules. A fuzzy module allows automatically fuzzy rule generation and fuzzy inference that allows permanent model testing for completeness. The production system is a mechanism for creating an expert module that trains the fuzzy system artificially with new learning sequences, like as states and control actions.

Knowledge acquisition and knowledge based control in bioprocess engineering

Knowledge acquisition is still a major bottle-neck with respect to efficient computer control design of knowledge based systems in bioprocess engineering. In this paper an approach towards the automatic generation of fuzzy rules is presented and applied to data of an industrial antibiotic fermentation. Fuzzy rules generated describe the relationship between the kinetics of the preculture and the antibiotic yield of the main culture. The terms used in these rules were derived by clustering employing the fuzzy-C-means method. In order to rate and select rules and finally to optimize parameters of membership functions of fuzzy variables different criteria are discussed in relation to the aim of the knowledge based control. Results are presented with respect to process monitoring. Genetic algorithms proved suitable for optimization procedures due to the existence of multiple local optima.

Optimized Fuzzy Rule Generation for Fermentation Control

An approach towards automatic generation of fuzzy rules is presented and applied to data from an industrial antibiotic fermentation. Fuzzy rules generated describe the influence of the kinetics of the preculture on the antibiotic yield at the end of the main culture. For feature extraction the data were classified by the fuzzy-C-means algorithm. Both, the selection of fuzzy rules and the optimal definition of membership functions for fuzzy values are carried out automatically. This requires criteria in order to assess the quality of the fuzzy rule set. The formulation of these criteria and consequently, the rule set obtained depend on the purpose of process control.

Algorithm for automatic generation of fuzzy rules applied to power system controllers

A new fuzzy algorithm for the identification of the input-output characteristic of a general system is applied to a power system controller. In particular, the method for the generation of fuzzy rules is applied to a fuzzy logic variable-structure FACTS controller for electrical power system stability improvement. The fuzzy algorithm allows one to obtain a simple analytical form for the input-output characteristic of the identified system. The identification is regarded as a nonlinear optimisation problem solved by means of the quasi-Newton optimisation method. The effectiveness of the proposed approach is verified through numerical simulations.

Automatic generation of fuzzy rules using hyper-elliptic-cone membership functions by genetic algorithms

We had presented an automatic generation technique for fuzzy rules using hyper-cone membership functions by genetic algorithms (GA). However, there remain some problems. The shape of fuzzy subsets ...

Automatic generation of fuzzy rules for reactive robot controllers

Basic tasks for navigation of autonomous vehicles can be performed as reactive behaviors, that directly map sensory data into control commands with no need of internal representations. Fuzzy systems can efficiently realize such a direct mapping by means of comprehensible linguistic rules. Automatic learning of rules from representative data simplifies the programming of the control system especially when dealing with dynamically changing environments. We present two methods for the automatic extraction of fuzzy rules from numerical data acquired by recording the choices of a human operator driving the vehicle in representative training situations. They are applied to build a reactive wall-follower, the first component of a more articulated control system for indoor navigation of a TRC Labmate mobile robot. The produced wall-followers are compared in terms of complexity and quality of behavior in actual navigation runs of the robot along arbitrarily shaped walls.

Closed Loop Fault Diagnosis Based on a Nonlinear Process Model and Automatic Fuzzy Rule Generation

Abstract In this contribution a new approach for fault detection and diagnosis (FDD) for nonlinear processes is presented. A nonlinear fuzzy model with transparent inner structure is used for the generation of relevant symptoms. The resulting symptom patterns are classified with a new self-learning classification structure based on fuzzy rules. The approach is successfully applied to a electro-pneumatic valve in a closed control loop.

ファジィニューラルネットワークの構築法とファジィルールの自動生成

ファジィニューラルネットワークの構築法とファジィルールの自動生成