Optimal Control Algorithms for Second Order Systems

Pelusi,

doi:10.3844/jcssp.2013.183.197

Cited by 34 publications

(20 citation statements)

References 18 publications

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“…Crossover should increase the average quality of the population. By choosing adequate crossover and mutation operators as well as an appropriate reduction mechanism, the probability that the GA results in a near-optimal solution in a reasonable number of iterations increases (Spears, 1995;Pelusi and Mascella, 2013).…”

Section: The Training Process: the Use Of Genetic Algorithmsmentioning

confidence: 99%

Weeds Identification Using Evolutionary Artificial Intelligence Algorithm

Tobal¹

2014

Journal of Computer Science

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In a world reached a population of six billion humans increasingly demand it for food, feed with a water shortage and the decline of agricultural land and the deterioration of the climate needs 1.5 billion hectares of agricultural land and in case of failure to combat pests needs about 4 billion hectares. Weeds represent 34% of the whole pests while insects, diseases and the deterioration of agricultural land present the remaining percentage. Weeds Identification has been one of the most interesting classification problems for Artificial Intelligence (AI) and image processing. The most common case is to identify weeds within the field as they reduce the productivity and harm the existing crops. Success in this area results in an increased productivity, profitability and at the same time decreases the cost of operation. On the other hand, when AI algorithms combined with appropriate imagery tools may present the right solution to the weed identification problem. In this study, we introduce an evolutionary artificial neural network to minimize the time of classification training and minimize the error through the optimization of the neuron parameters by means of a genetic algorithm. The genetic algorithm, with its global search capability, finds the optimum histogram vectors used for network training and target testing through a fitness measure that reflects the result accuracy and avoids the trial-and-error process of estimating the network inputs according to the histogram data.

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Section: The Training Process: the Use Of Genetic Algorithmsmentioning

confidence: 99%

Weeds Identification Using Evolutionary Artificial Intelligence Algorithm

Tobal¹

2014

Journal of Computer Science

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“…By the law (2), it follows that the acceleration of the agent i at time t and in direction d-th, a d i (t), is given by (7), where M ii is the inertial mass of the i-th agent.…”

Section: The Gravitational Search Algorithmmentioning

confidence: 99%

“…Among the intelligent evolutionary optimization methods, the Genetic Algorithms (GA) [4] are heuristic approaches well suited to solve complex computational problems [5][6][7][8][9]. Another evolutionary algorithm is the Particle Swarm Optimization (PSO), which depends on the simulation of social behavior [10].…”

Section: Introductionmentioning

confidence: 99%

Revised Gravitational Search Algorithms Based on Evolutionary-Fuzzy Systems

2017

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Abstract:The choice of the best optimization algorithm is a hard issue, and it sometime depends on specific problem. The Gravitational Search Algorithm (GSA) is a search algorithm based on the law of gravity, which states that each particle attracts every other particle with a force called gravitational force. Some revised versions of GSA have been proposed by using intelligent techniques. This work proposes some GSA versions based on fuzzy techniques powered by evolutionary methods, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE), to improve GSA. The designed algorithms tune a suitable parameter of GSA through a fuzzy controller whose membership functions are optimized by GA, PSO and DE. The results show that Fuzzy Gravitational Search Algorithm (FGSA) optimized by DE is optimal for unimodal functions, whereas FGSA optimized through GA is good for multimodal functions.

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“…This research proposes an equivalent fuzzy PID controller which has a simple PID structure design with a 3-dimensional fuzzy rule table, instead of the combination of different fuzzy PID structural elements or a hybrid controller structure [14]. Moreover, to achieve optimal control performance for a FLC, some artificial intelligent techniques such as Genetic Algorithm and Neural Network are efficient approaches [15,16]. This inspires us in the future to propose nonlinear factors for tuning the membership functions to develop an optimal fuzzy PID controller design with less parameters.…”

Section: Introductionmentioning

confidence: 99%

“…But based on this study, a fuzzy PID controller may outperform a conventional PID controller quickly by fine-tuning the MFs of the fuzzy variables. We have found that some learning-based techniques or evolutionary algorithms have been applied in the optimal FLC design [14][15][16]. Experienced researchers should agree on the importance of setting initial values or weights in the learning system, which will greatly influence the learning results and convergence speed.…”

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confidence: 99%

Equivalence between Fuzzy PID Controllers and Conventional PID Controllers

et al. 2017

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This paper proposes the equivalence between fuzzy Proportional-Integral-Derivative (PID) controllers and conventional PID controllers. A well-designed conventional PID controller, with the help of the proposed method, can be rapidly transformed to an equivalent fuzzy logic controller (FLC) by observing and defining the operating ranges of the input/output of the controller. Furthermore, the knowledge base of the proposed equivalent fuzzy PID controller is represented as a cube fuzzy associative memory (FAM), instead of a combination of PD-type and PI-type FLCs in most research. Simulation results show the feasibility of the proposed technique, both in continuous and discrete time. Since the design techniques of conventional linear PID controllers have matured, they can act as preliminary expert knowledge for nonlinear FLCs designs. Based on the proposed equivalence relationship, the designer can further tune the membership functions of fuzzy variables in the control rules to exhibit the nonlinearity of a FLC and yield more satisfactory system responses in an efficient way.

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Optimal Control Algorithms for Second Order Systems

Cited by 34 publications

References 18 publications

Weeds Identification Using Evolutionary Artificial Intelligence Algorithm

Weeds Identification Using Evolutionary Artificial Intelligence Algorithm

Revised Gravitational Search Algorithms Based on Evolutionary-Fuzzy Systems

Equivalence between Fuzzy PID Controllers and Conventional PID Controllers

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