مشخصات پژوهش

عنوان	Optimization of quantum-inspired neural network using memetic algorithm for function approximation and chaotic time series prediction
نوع پژوهش	مقاله چاپ‌شده در مجلات علمی
کلیدواژه‌ها	Memetic algorithm Genetic algorithm Quantum-inspired neural network Function approximation Time series prediction
چکیده	Heuristic and deterministic optimization methods are extensively applied for the training of artificial neural networks. Both of these methods have their own advantages and disadvantages. Heuristic stochastic optimization methods like genetic algorithm perform global search, but they suffer from the problem of slow convergence rate near global optimum. On the other hand deterministic methods like gradient descent exhibit a fast convergence rate around global optimum but may get stuck in a local optimum. Motivated by these problems, a hybrid learning algorithm combining genetic algorithm (GA) with gradient descent (GD), called HGAGD, is proposed in this paper. The new algorithm combines the global exploration ability of GA with the accurate local exploitation ability of GD to achieve a faster convergence and also a better accuracy of final solution.
پژوهشگران	سهیل گنجه فر (نفر اول)، مرتضی توفیقی (نفر دوم)