کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
405578 677681 2010 4 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Quantum inspired PSO for the optimization of simultaneous recurrent neural networks as MIMO learning systems
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Quantum inspired PSO for the optimization of simultaneous recurrent neural networks as MIMO learning systems
چکیده انگلیسی

Training a single simultaneous recurrent neural network (SRN) to learn all outputs of a multiple-input–multiple-output (MIMO) system is a difficult problem. A new training algorithm developed from combined concepts of swarm intelligence and quantum principles is presented. The training algorithm is called particle swarm optimization with quantum infusion (PSO-QI). To improve the effectiveness of learning, a two-step learning approach is introduced in the training. The objective of the learning in the first step is to find the optimal set of weights in the SRN considering all output errors. In the second step, the objective is to maximize the learning of each output dynamics by fine tuning the respective SRN output weights. To demonstrate the effectiveness of the PSO-QI training algorithm and the two-step learning approach, two examples of an SRN learning MIMO systems are presented. The first example is learning a benchmark MIMO system and the second one is the design of a wide area monitoring system for a multimachine power system. From the results, it is observed that SRNs can effectively learn MIMO systems when trained using the PSO-QI algorithm and the two-step learning approach.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 23, Issue 5, June 2010, Pages 583–586
نویسندگان
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