用連續(xù)回歸神經(jīng)網(wǎng)絡(luò)求解泛函極值問題
A CONTINUOUS TIME RECURRENT NEURAL NETWORK BASED METHOD TO SOLVE FUNCTIONAL MINIMIZATION PROBLEM
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摘要: 針對信息科學(xué)和控制理論中經(jīng)常涉及的一類泛函極值問題，提出基于連續(xù)回歸神經(jīng)網(wǎng)絡(luò)的求解方法。推導(dǎo)了求解泛函的連續(xù)BPTT算法，進(jìn)而對該算法進(jìn)行改進(jìn)，得出一種在線學(xué)習(xí)算法，為并行實(shí)現(xiàn)打下了基礎(chǔ)．Abstract: In this paper, the continuous time recurrent neural network is proposed to solve the functional minimization problem, which is often involved in estimation and control. At first, the continuous time BPTT algorithm corresponding to the problem is presented. Then,an on-line algorithm based on the amendments of the BPTT algorithm is discussed. This on-line algorithm paves the way for parallel realization.
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