Optimal Fuzzy Model Construction with Statistical Information using Genetic Algorithm

Fuzzy rule based models have a capability to approximate any continuous function to any degree of accuracy on a compact domain. The majority of FLC design process relies on heuristic knowledge of experience operators. In order to make the design process automatic we present a genetic approach to learn fuzzy rules as well as membership function parameters. Moreover, several statistical information criteria such as the Akaike information criterion (AIC), the Bhansali-Downham information criterion (BDIC), and the Schwarz-Rissanen information criterion (SRIC) are used to construct optimal fuzzy models by reducing fuzzy rules. A genetic scheme is used to design Takagi-Sugeno-Kang (TSK) model for identification of the antecedent rule parameters and the identification of the consequent parameters. Computer simulations are presented confirming the performance of the constructed fuzzy logic controller

Automatic synthesis of fuzzy systems: An evolutionary overview with a genetic programming perspective

Studies in Evolutionary Fuzzy Systems (EFSs) began in the 90s and have experienced a fast development since then, with applications to areas such as pattern recognition, curve‐fitting and regression, forecasting and control. An EFS results from the combination of a Fuzzy Inference System (FIS) with an Evolutionary Algorithm (EA). This relationship can be established for multiple purposes: fine‐tuning of FIS's parameters, selection of fuzzy rules, learning a rule base or membership functions from scratch, and so forth. Each facet of this relationship creates a strand in the literature, as membership function fine‐tuning, fuzzy rule‐based learning, and so forth and the purpose here is to outline some of what has been done in each aspect. Special focus is given to Genetic Programming‐based EFSs by providing a taxonomy of the main architectures available, as well as by pointing out the gaps that still prevail in the literature. The concluding remarks address some further topics of current research and trends, such as interpretability analysis, multiobjective optimization, and synthesis of a FIS through Evolving methods

Intelligent controllers for vechicle suspension system using magnetorheological damper

Semi-active suspension control with magnetorheological (MR) damper is one of the most fascinating systems being studied in improving the vehicle ride comfort. This study aims to investigate the development of intelligent controllers for vehicle suspension system using MR damper, namely, the proportional-integral-derivative (PID) and fuzzy logic (FL) controllers optimized using particle swarm optimization (PSO), firefly algorithm (FA) and advanced firefly algorithm (AFA). Since the conventional optimization method always has a problem in identifying the optimum values and it is time consuming, the evolutionary algorithm is the best approach in replacing the conventional method as it is very efficient and consistent in exploring the values for every single space. The PSO and FA are among of the evolutionary algorithms which have been studied in this research. Nevertheless, the weakness of FA such as getting trapped into several local minima is an attractive area that has been focused more as a possible improvement during the evolutionary process. Thus, a new algorithm based on the improvement of the original FA was introduced to improve the solution quality of the FA. This algorithm is called advanced firefly algorithm. A parametric modelling technique known as Spencer model was proposed and employed to compute the dynamic behaviour of the MR damper system. The Spencer model was experimentally validated and conducted to capture the behaviour of the Lord RD-1005-3 MR damper with the same excitation input. A simulation of a semi-active suspension system was developed within MATLAB Simulink environment. The effectiveness of all control schemes were investigated in two major issues, namely the ability of the controller to reject the unwanted motion of the vehicle and to overcome the damping constraints. The result indicates that, the PID-AFA control scheme is more superior as compared to the PID-PSO, PID-FA, FL-PSO, FL-FA, FL-AFA and passive system with up to 27.1% and 19.1% reduction for sprung mass acceleration and sprung mass displacement, respectively. Finally, the performance of the proposed intelligent control schemes which are implemented experimentally on the developed quarter vehicle suspension test rig shows a good agreement with the results of the simulation study. The proposed control scheme of PID-AFA has reduced the sprung mass acceleration and sprung mass displacement over the FL-AFA and passive system up to 28.21% and 16.9%, respectively

Fuzzy approach performance of shortterm electricity load forecasting in Malaysia

Many activities (such as economic, education and etc.) would paralyse with limited supply of electricity but surplus contribute to high operating cost.Therefore electricity load forecasting is important in order to avoid shortage or excess.Many techniques have been employed in forecasting short term electricity load.They can be classifies either by statistical or artificial intelligent (AI) or hybrid of those two techniques; Statistical techniques and AI techniques. Electricity load demand is influenced by many factors, such as weather, economic, social activities and etc.The relation between load demand and the independent variables is complex and it is not always possible to fit the load curve using statistical models.The complexity and uncertainties of this problem appear suitable for fuzzy methodologies.Hence, the Fuzzy approach was used to forecast electricity load demand.Previous findings showed festive celebration has effect on shortterm electricity load forecasting.Being a multi culture country Malaysia has many major festive celebrations (EidulFitri, Chinese New Year, Deepavali and etc.) but they are moving holidays due to non-fixed dates on the Gregorian calendar.Therefore, the performance of fuzzy approach in forecasting electricity loads when considering the presence of moving holidays was studied.Autoregressive Distributed Lag (ARDL) model was estimated using simulated data by including model simplification concept (manual or automatic), day types (weekdays or weekend), public holidays and lags of electricity load.The result indicated that day types, public holidays and several lags of electricity load were significant in the model.Overall, model simplification improves fuzzy performance due to less variables and rules

Optimisation of a water company’s waste pumping asset base with a focus on energy reduction

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonWater companies use a significant quantity of electricity for the operation of their clean and wastewater assets. Rising energy prices have led to higher energy bills within the water companies, which has increased operating costs. Thus, improvements in demand side energy management are needed to increase efficiency and reduce costs, which forms the premise for this research project. Thames Water Utilities Ltd has identified that improvements in demand side energy management is required and is currently researching various methods to reduce energy consumption. One initiative included the upgrade of a variety of site telemetry assets. By deploying these new telemetry assets, Thames Water Utilities Ltd are more able to liberate the asset data and as such, be able to make informed decisions on how better to control and optimise the target sites, which is where this research project has seen further opportunities. This enhanced telemetry and SCADA infrastructure will enable successful research to further develop an intelligent integrated system that tackles pump scheduling and process control with the emphasis on energy management. The use of modern techniques, such as artificial intelligence, to optimise the network operation is gradually gaining traction. The balance between implementing new technology (with the benefits it may bring) and reluctance to change from the incumbent operating model will always provide challenges in the technology adoption agenda. The main work of this research project included the physical surveying of a wastewater hydraulic catchment, inclusive of all wet well dimensions, lidar overlays, and pump electrical power characteristics. These survey results where then able to be programmed by the research into the company’s' hydraulic model to enable a higher degree of accuracy in the modelling, as well as enabling electrical power as a measurable output. From here, the model was then able to be optimised, focussing on electrical energy as an output variable for reduction. The research concluded that electrical energy consumption over time can be reduced using the aforementioned strategies and as such recommends further work to move from the model environment to physical architecture. It does so with the key message that risk tolerances on water levels must be pre-agreed with hydraulic specialists prior to deployment

Utilização da lógica fuzzy para determinação da velocidade limite em rodovias

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Civil e Ambiental, 2018.A determinação da velocidade limite de um trecho rodoviário, no Brasil, é um processo essencialmente subjetivo, pois não há uma metodologia clara para a consideração dos fatores que possam influenciar o estabelecimento de um valor para a velocidade. Uma das alternativas para superar esta dificuldade está no desenvolvimento de um sistema especialista, que possa reduzir as incertezas do procedimento e subsidie a tomada de decisão pelo responsável por definir as velocidades. Nesse contexto, dadas as características do problema, a lógica fuzzy se mostra como uma ferramenta adequada por simular o raciocínio humano englobando o conceito de variáveis linguísticas, pois as variáveis para determinação da velocidade limite, em sua maioria, não possuem quantificação clara e objetiva. Portanto, o objetivo deste trabalho foi conceber um sistema especialista, com emprego da lógica fuzzy, que viabilize a definição da velocidade limite em rodovias. Primeiramente, houve a necessidade de identificar as variáveis mais relevantes para a determinação de velocidade limite em rodovias, procedimento para o qual foi utilizado o método fuzzy multicritério para tomada de decisão. Em seguida, o sistema especialista fuzzy foi elaborado com base no conhecimento obtido junto à especialistas na área por meio de um questionário, que permitiu a construção das funções de pertinência e da base de regras. As saídas do sistema foram parcialmente satisfatórias quando comparadas com as respostas que subsidiaram a construção do controlador fuzzy. Entretanto, notou-se que a lógica fuzzy se destaca como uma alternativa para a modelagem da determinação da velocidade limite em rodovias, sendo necessários estudos futuros neste tema para o aperfeiçoamento da técnica.The determination of speed limits for a highway segment, in Brazil, is essentially a subjective procedure, since there is not a clear methodology for considering the factors that may influence the settlement of a value for speed. One of the alternatives to overcome this difficulty is in the development of an expert system that might reduce the uncertainties of the procedure and subsidize the decision-making process of the expert responsible for the speed settlement. In these circumstances, given the problem’s characteristics, the fuzzy logic turns up as a tool proper for simulating the human reasoning, encompassing the concept of linguistic variables. This is important because the variables for the determination of speed limits mostly do not have a clear and objective quantification. Therefore, the aim of this work was to develop an expert system, with the usage of the fuzzy logic, that may allow the settlement of highway speed limits. Firstly, there was the need of identifying the most relevant variables for the settlement of highway speed limits, procedure in which the fuzzy multiple criteria decision-making method was employed. After that, the fuzzy system was conceived based on the knowledge acquired from experts through a questionnaire. This data made possible the construction of membership functions and fuzzy rules. The system outputs have been partially satisfactory when compared to the responses that allowed the conception of the fuzzy control. However, it can be seen that the fuzzy logic stands out as an alternative to modeling the settlement of highway speed limits, and future research is needed on such topic so that the technique can be ameliorated

Reinforcement Learning for Parameter Control of Image-Based Applications

The significant amount of data contained in digital images present barriers to methods of learning from the information they hold. Noise and the subjectivity of image evaluation further complicate such automated processes. In this thesis, we examine a particular area in which these difficulties are experienced. We attempt to control the parameters of a multi-step algorithm that processes visual information. A framework for approaching the parameter selection problem using reinforcement learning agents is presented as the main contribution of this research. We focus on the generation of state and action space, as well as task-dependent reward. We first discuss the automatic determination of fuzzy membership functions as a specific case of the above problem. Entropy of a fuzzy event is used as a reinforcement signal. Membership functions representing brightness have been automatically generated for several images. The results show that the reinforcement learning approach is superior to an existing simulated annealing-based approach. The framework has also been evaluated by optimizing ten parameters of the text detection for semantic indexing algorithm proposed by Wolf et al. Image features are defined and extracted to construct the state space. Generalization to reduce the state space is performed with the fuzzy ARTMAP neural network, offering much faster learning than in the previous tabular implementation, despite a much larger state and action space. Difficulties in using a continuous action space are overcome by employing the DIRECT method for global optimization without derivatives. The chosen parameters are evaluated using metrics of recall and precision, and are shown to be superior to the parameters previously recommended. We further discuss the interplay between intermediate and terminal reinforcement