Evolutionary design of Fuzzy Logic Controllers with the techniques Artificial Neural Network and Genetic Algorithm for cart-pole problem

In the last few decades the Genomic data and research associated to these Genomic Data sets are folded in to several times. Bioinformatics is the Application of Computer technology in the field of medicine or drug research. As these Genomic data are folded into a huge multiples so there is the necessity of computer and an effective computing technology to process these data set for better and optimum result in Drug design or medical/medicine research. There are various hard computing and Soft Computing technologies that are being applied in this field by various researchers. Soft Computing technologies like Artificial Neural Network, Fuzzy Logic, Genetic Algorithms and other hybrid soft computing technologies like swarm optimization, Ant Colony Network are also very popular and upcoming technologies for these research. Protein Structure prediction is the prediction of three dimension structure form its amino acid sequence and a Protein molecule contain several amino acid sequences which changes it shape in the effect of external agents and application of various drugs from time to time. Genetic Algorithm is a model of machine learning which represent the behaviour of the system in the form of a metaphor of the processes. So Genetic Algorithm can be applied in the field of Protein Structure Prediction to predict the structure and for the creation of a metaphor of the changes in the shape and size of the protein molecules or the amino acid sequences. So in this research article we have applied Genetic Algorithm for the purpose of Protein Structure prediction. For a case study purpose we have taken the example of a 4HU7 protein data set and we have also provides the simulation result for this purpose.

Global change study is an interdisciplinary and comprehensive research activity with international cooperation, arising in 1980s, with the largest scopes. The interaction between land use and cover change, as a research field with the crossing of natural science and social science, has become one of core subjects of global change study as well as the front edge and hot point of it. It is necessary to develop research on land use and cover change in urbanization process and build an analog model of urbanization to carry out description, simulation and analysis on dynamic behaviors in urban development change as well as to understand basic characteristics and rules of urbanization process. This has positive practical and theoretical significance for formulating urban and regional sustainable development strategy. The effect of urbanization on land use and cover change is mainly embodied in the change of quantity structure and space structure of urban space, and LUCC model in urbanization process has been an important research subject of urban geography and urban planning. In this paper, based upon previous research achievements, the writer systematically analyzes the research on land use/cover change in urbanization process with the theories of complexity science research and intelligent computation; builds a model for simulating and forecasting dynamic evolution of urban land use and cover change, on the basis of cellular automation model of complexity science research method and multi-agent theory; expands Markov model, traditional CA model and Agent model, introduces complexity science research theory and intelligent computation theory into LUCC research model to build intelligent computation-based LUCC model for analog research on land use and cover change in urbanization research, and performs case research. The concrete contents are as follows: 1. Complexity of LUCC research in urbanization process. Analyze urbanization process in combination with the contents of complexity science research and the conception of complexity feature to reveal the complexity features of LUCC research in urbanization process. Urban space system is a complex economic and cultural phenomenon as well as a social process, is the comprehensive characterization of urban society, economy and culture, and is a complex space system formed by society, economy and nature. It has dissipative structure characteristics, such as opening, dynamics, self-organization, non-balance etc. Traditional model cannot simulate these social, economic and natural driving forces of LUCC including main feedback relation from LUCC to driving force. 2. Establishment of Markov extended model of LUCC analog research in urbanization process. Firstly, use traditional LUCC research model to compute change speed of regional land use through calculating dynamic degree, exploitation degree and consumption degree of land use; use the theory of fuzzy set to rewrite the traditional Markov model, establish structure transfer matrix of land use, forecast and analyze dynamic change and development trend of land use, and present noticeable problems and corresponding measures in urbanization process according to research results. 3. Application of intelligent computation research and complexity science research method in LUCC analog model in urbanization process. On the basis of detailed elaboration of the theory and the model of LUCC research in urbanization process, analyze the problems of existing model used in LUCC research (namely, difficult to resolve many complexity phenomena in complex urban space system), discuss possible structure realization forms of LUCC analog research in combination with the theories of intelligent computation and complexity science research. Perform application analysis on BP artificial neural network and genetic algorithms of intelligent computation and CA model and MAS technology of complexity science research, discuss their theoretical origins and their own characteristics in detail, elaborate the feasibility of them in LUCC analog research, and bring forward improvement methods and measures on existing problems of this kind of model. 4. Establishment of LUCC analog model in urbanization process based on theories of intelligent computation and complexity science. Based on the research on abovementioned BP artificial neural network, genetic algorithms, CA model and multi-agent technology, put forward improvement methods and application assumption towards their expansion on geography, build LUCC analog model in urbanization process based on CA model and Agent model, realize the combination of learning mechanism of BP artificial neural network and fuzzy logic reasoning, express the regulation with explicit formula, and amend the initial regulation through self study; optimize network structure of LUCC analog model and methods and procedures of model parameters with genetic algorithms. In this paper, I introduce research theory and methods of complexity science into LUCC analog research and presents LUCC analog model based upon CA model and MAS theory. Meanwhile, I carry out corresponding expansion on traditional Markov model and introduce the theory of fuzzy set into data screening and parameter amendment of improved model to improve the accuracy and feasibility of Markov model in the research on land use/cover change.

Over the past few years, a number of deepwater projects that use steel lazy wave risers have been commissioned or are under development. Steel lazy wave risers have an advantage over steel catenary risers as they offer flexibility of use with a floater having severe motion such as FPSO. They also impart lower loads at the interface with the floater compared to a traditional steel catenary riser, and hence can be used in deeper waters. Therefore, design of steel lazy wave risers has gained importance over the years as exploration of oil happens in ever deeper waters. In this paper, artificial neural networks and genetic algorithm are used to automatically generate a steel lazy wave riser design. A dataset of optimized designs of steel lazy wave risers for various inputs such as water depth, pipe OD, wall thickness etc. are generated using genetic algorithm. This dataset is used to train a neural network to automatically output a steel lazy wave riser design. The SLWR configuration that is automatically generated can be used as a starting point for conceptual and pre-FEED studies and help engineers come up with an initial SLWR design capturing the basic requirements without going through rigorous analyses. It has potential for cost savings and meeting schedule demands of fast paced projects as it will speed up the steel lazy wave risers’ design.

This work approaches relative aspects to the alarm processing problem and fault diagnosis in system level, having as purpose filter the alarms generated during a outage and identify the equipment under fault. A methodology was developed using Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in order to resolve the problem. This procedure had as initiative explore the GA capacity to deal with combinatory problems, as well as the ANN processing speed and generalization capacity. Such strategy favors a fast and robust solution.

In this paper, a novel approach is described to design an interdigital capacitor (IDC) using artificial neural networks (ANNs) and genetic algorithm (GA). The scattering parameters of the training samples are computed by the finite‐difference time‐domain (FDTD) and ANN is applied to describe the models of IDC. According to the engineering requirement, the dimensions of IDC can be designed with GA using the trained ANN models. This design procedure is proved to be time saving and of high accuracy. © 2003 Wiley Periodicals, Inc. Microwave Opt Technol Lett 38: 231–235, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.11023

Today, there's a synergy beginning to form among neural nets (NNs), fuzzy logic (FL) and genetic algorithms (GAs). This paper reviews developments in this respect. Many designs of knowledge-based and associated learning systems using the combination of NNs and FL abilities have been presented. Some methods for the integration of GAs with fuzzy systems are described. Afterwards, work on hybrid systems of GAs and NNs is discussed. Finally, some advantages obtained through the fusion of the FL, NNs and GAs are emphasized, and possible ways of combining the three are developed.

This paper studies the use of Genetic Algorithms (GA) in the design of Fuzzy Logic Controllers (FLC) and show how population size, probability of crossover and rate of mutation can effect the performance of the GA. The comparison of various parameters shows that GA is helpful in improving the performance of FLC. A fuzzy logic is fully defined by its membership function. What is the best to determine the membership function is the first question that has be tackled. Thus it is important to select the accurate membership functions but these methods possess one common weakness where conventional FLC use membership function generated by human operators. The membership function selection process is done with trial and error and it runs step by step which is too long in completing the problem. This research develops a system that may help users to determine the membership function of FLC using the technique of GA optimization for the fastest processing in completing the problems. The data collection is based on the simulation results and the results refer to the transient response specification is maximum overshoot. From the results presented, the system which we developed is very helpful to determine membership function and it is clear that the GA is very promising in improving the performance of the FLC to get more accurate in order to find the optimum result.

In this study, we proposed a new approach in estimating a minimum value of machining performance. In this approach, artificial neural network (ANN) and genetic algorithm (GA) techniques were integrated in order to search for a set of optimal cutting condition points that leads to the minimum value of machining performance. Three machining cutting conditions for end milling operation that were considered in this study are speed (v), feed (f) and radial rake angle (γ). The considered machining performance is surface roughness (R a). The minimum R a value at the optimal v, f and γ points was expected from this approach. Using the proposed approach, named integrated ANN–GA, this study has proven that R a can be estimated to be 0.139 µm, at the optimal cutting conditions of f = 167.029 m/min, v = 0.025 mm/tooth and γ = 14.769°. Consequently, the ANN–GA integration system has reduced the R a value at about 26.8%, 25.7%, 26.1% and 49.8%, compared to the experimental, regression, ANN and response surface method results, respectively. Compared to the conventional GA result, it was also found that integrated ANN–GA reduced the mean R a value and the number of iterations in searching for the optimal result at about 0.61% and 23.9%, respectively.

Spam detection is one of the major problems which considered by many researchers by different developed strategies. Artificial Neural Network (ANN) is one of many others being proposed. However designing an ANN is a difficult task as it requires setting of ANN structure and tuning of some complex parameters. In this study, ANN was hybridized with Genetic algorithm (GA) in order to optimize the performance of ANN for spam detection. GA was used to determine some ANN parameters and suggest optimum weights to efficiently enhance the ANN learning. Experimental results show that the hybrid ANN and GA has superior performance when compared to conventional ANN.

Design of fuzzy logic controller for smart base isolation system using genetic algorithm

The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques to solve different engineering problems. Besides, Short Term Electrical Load Forecasting (STLF) is one of the important concerns of power systems and accurate load forecasting is vital for managing supply and demand of electricity. This study estimates short term electricity loads of Iran by means of Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Networks (ANN) and Genetic Algorithm (GA) which are the most successful AI techniques in this field. In order to improve forecasting accuracy, all AI techniques are equipped with preprocessing concept, and effects of this concept on performance of each AI technique are investigated. Finally, outcomes of the approaches are evaluated and compared by means of the mean absolute percentage error (MAPE). Results show that data preprocessing can significantly improve performance of the AI techniques. Meanwhile, ANFIS outcomes are more approximate to the actual loads than those of ANN and GA, so it can be considered as a suitable tool to deal with STLF problems.

Technological advances and high cost of ownership have resulted in considerable interest in advanced maintenance techniques. Quantifying fault and consequently availability requires the use of gas turbine and combined cycle models able to undertake appropriate diagnostics and life cycle costing. These are complex areas as they include the simulation of such issues as performance and assessment of degraded gas turbines, life usage and risk analysis. This paper describes how the recent developments in engine diagnostics using advanced techniques like Artificial Neural Networks (ANN) and Genetic Algorithm (GA) based technique have opened new opportunities in the field of engine fault diagnostics. It also discusses the potential of advanced engine diagnostics using such features as ANN and GA in contributing to the management of availability for industrial gas turbines.

The Levenberg–Marquardt (LM) backpropagation optimization algorithm, an artificial neural network algorithm, is used in this study to perform integrated numerical computing to evaluate the electromagnetohydrodynamic bioconvection flow of micropolar nanofluid with thermal radiation and stratification. The model is then reduced to a collection of boundary value problems, which are solved with the help of a numerical technique and the proposed scheme, i.e., the LM algorithm, which is an iterative approach to determine the minimum of a nonlinear function defined as the sum of squares. As a blend of the steepest descent and the Gauss–Newton method, it has become a typical approach for nonlinear least-squares problems. Furthermore, the stability and consistency of the algorithm are ensured. For validation purposes, the results are also compared with those of previous research and the MATLAB bvp4c solver. Neural networking is also utilized for velocity, temperature, and concentration profile mapping from input to output. These findings demonstrate the accuracy of forecasts and optimizations produced by artificial neural networks. The performance of the bvp4c solver, which is used to reduce the mean square error, is used to generalize a dataset. The artificial neural network-based LM backpropagation optimization algorithm operates using data based on the ratio of testing (13%), validation (17%), and training (70%). This stochastic computing work presents an activation log-sigmoid function based LM backpropagation optimization algorithm, in which tens of neurons and hidden and output layers are used for solving the learning language model. The overlapping of the results and the small computed absolute errors, which range from 10−3 to 10−10 and from 106 to 108 for each model class, indicate the accuracy of the artificial neural network-based LM backpropagation optimization algorithm. Furthermore, each model case’s regression performance is evaluated as if it were an ideal model. In addition, function fitness and histogram are used to validate the dependability of the algorithm. Numerical approaches and artificial neural networks are an excellent combination for fluid dynamics, and this could lead to new advancements in many domains. The findings of this research could contribute to the optimization of fluid systems, resulting in increased efficiency and production across various technical domains.

Intelligent control is a class of control techniques that use various AI computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, evolutionary computation and genetic algorithms. In computer science and related fields, artificial neural networks are computational models inspired by animals’ central nervous systems (in particular the brain) that are capable of machine learning and pattern recognition. They are usually presented as systems of interconnected “neurons” that can compute values from inputs by feeding information through the network. Like other machine learning methods, neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition.

Attempts are being made to enhance the drive performance by intelligent control using fuzzy logic (FL) and neural network techniques. One of the frequently discussed applications of artificial intelligence in control is the replacement of a standard proportional plus integral (PI) speed controller with an FL or artificial neural network (ANN) speed controller. Regardless of all the work, it appears that a thorough comparison of the drive behavior under PI, FL, and ANN speed control is necessary. This article attempts to compare PI, fuzzy, and ANN controllers that are implemented in an embedded system for closed-loop speed control of DC drive fed by a buck-type DC–DC power converter. The PI controller is designed based on the small signal modeling of the system. The PI-like fuzzy controller structure is considered for comparison. Two ANN controllers are designed. One controller uses training data obtained from the simulation of a fuzzy controller and the other uses training data from the simulation of a PI controller. The performance of the controllers is studied for a variety of operating conditions, such as step change in speed command and step change in load torque. The parameters selected for the comparison are the steady-state error and the rise time of the response. It is shown that ANN speed controllers provide a superior speed response in terms of rise time and the steady-state error compared to PI and FL controllers. This advantage arises from the fact that the neural network has the property of generalization and the control surface of the neural controller is smooth. The designed neural network controller is simple, with three neurons only, and so it is best suited for embedded system implementation. It is also found that the ANN controller trained with the training data from a PI controller has a better response compared to the ANN controller trained with data from a fuzzy controller.