کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4942718 1437418 2017 20 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Metaheuristic design of feedforward neural networks: A review of two decades of research
ترجمه فارسی عنوان
طراحی مورخانه شبکه های عصبی فیدورس: بررسی دو دهه تحقیق
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Engineering Applications of Artificial Intelligence - Volume 60, April 2017, Pages 97-116
نویسندگان
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