Present study considers multidimensional function to improve a drag wind turbine. Artificial Neural Network was assigned to define a genetic algorithm in artificial intelligence pdf function. Cost function is optimized by Genetic Algorithm to obtain the optimum design.
Optimum geometry has been simulated by CFD to get the off design performance. A more tranquil flow-field around the optimal rotor is observed. Power coefficient, the most significant criterion for evaluating the performance of Savonius rotor is a multi-dimensional function of numerous parameters like overlap ratio, number of stages, blade rotation, etc. All these parameters have been examined separately and an approximate span in which optimum performance can be attained is proposed for each one. Furthermore, neither any attempt on scrutinizing this range accurately nor any investigations on probing the probability of existence of any interacting relation among these parameters have been reported so far. Using computational intelligence, an accurate study toward this span and a probable relation among these parameters has been conducted.
Power coefficient is considered as a function of six independent input parameters, according to experimental data extracted from a related paper. An Artificial Neural Network has been assigned to investigate a logical interaction among dependent and independent variables and define a cost function based on same empirical data. This function is then optimized by Genetic Algorithm and best amount for each parameter has been determined. Suggested geometry and flow field conditions have then been simulated by Computational Fluid Dynamics and acceptable agreement is detected. Check if you have access through your login credentials or your institution. The stock market, which has been investigated by various researchers, is a rather complicated environment.
However, the latter plays a critical role in the stock market environment. An example based on the Taiwan stock market is utilized to assess the proposed intelligent system. Evaluation results indicate that the neural network considering both the quantitative and qualitative factors excels the neural network considering only the quantitative factors both in the clarity of buying-selling points and buying-selling performance. Science fiction sometimes emphasizes the dangers of artificial intelligence, and sometimes its positive potential. The notion of advanced robots with human-like intelligence has been around for decades. There is no security against the ultimate development of mechanical consciousness, in the fact of machines possessing little consciousness now.
A jellyfish has not much consciousness. Reflect upon the extraordinary advance which machines have made during the last few hundred years, and note how slowly the animal and vegetable kingdoms are advancing. The more highly organized machines are creatures not so much of yesterday, as of the last five minutes, so to speak, in comparison with past time. Various scenarios have been proposed for categorizing the general themes dealing with artificial intelligence in science fiction. The main approaches are AI dominance, Human dominance and Sentient AI. The literature of science fiction and fantasy is extensive and includes many subgenres which include artificial intelligence as a recurrent theme. As such, themes of artificial intelligence in fiction can adopt utopian themes of AI entities help humans and human society, or, or themes of dystopia where AI entities become antagonists of humans and human society as a whole.