Neural network with unbounded activation functions is universal approxima- tor. Applied and Computational Harmonic Analysis, 2017.Sho Sonoda and Noboru Murata. Neural network with unbounded activation functions
2. 结构 前向神经网络(Feed-forward Neural Network)是一种多层的网络结构,非常典型的就是三层结构:输入层、隐藏层和输出层。层与层之间用权值(连线)连接。一般只有前一层的值才能作为后一层的值的输入,而反过来不能,也就是说不能有反馈(Feed-back),这也是其名字的又来。这个结构非常重要,因为名声赫赫的BP算...
Neural Network with Unbounded Activation Functions is Universal Approximator Sho Sonoda,Noboru Murata Full-Text Cite this paper Add to My Lib Abstract: This paper presents an investigation of the approximation property of neural networks with unbounded activation functions, such as the rectified line...
Fuzzy Logic Universal Approximator Consider a multi-input single-output (MISO) fuzzy logic system with singleton fuzzifier, COG defuzzifier, and Gaussian-type membership functions: (8.13)μAij(xi)=ρijexp[−12(xi−x¯ijσj)2] where xi is the ith component of the fuzzy input vector ...
Universal classifierIn this article, a competitive functional link artificial neural network (C-FLANN) is proposed for function approximation and classification problems. In contrast to the traditional functional link ardoi:10.1007/s00500-017-2644-1Lotfi, Ehsan...
27 September 2016 accepted: 01 February 2017 Published: 10 March 2017 Filippo Maria Bianchi1, Lorenzo Livi2, Cesare Alippi3,4 & Robert Jenssen1 A recurrent neural network (RNN) is a universal approximator of dynamical systems, whose performance often depends on sensitive hyperparameter...
While the neural network presented in equation (3) can be proven to be a universal approximator as it is an approximation of an ODE system1,2, in its current form, it has trainability issues which we point out and resolve shortly. Resolving the gradient issues The exponential term in ...
If either bias node is removed, the net is no longer a universal approximator. It looks like you want 테마복사 Nw = I*H+(H+1)*O with 테마복사 I*H = 4 H*O = 2 O = 1 Consequently, O=1, H=2, I = 2 and...
Importantly, it is also proved that PGF network (PGFN) having one hidden layer is capable of universal approximation. As a universal approximator, PGFN gives an informal way of bridging the gap between MLP and RBFN. The experiments report comparison between training time and classification accu...
文中多处用到了universal approximators.主要内容引理1FF定义了universal approximators, 即同一定义域内的任意函数ff都能用FF中的元素来逼近. σ(fθ)σ(fθ)则是将值域进行了扩展, 而这并不影响其universal approximator的性质.定理1证明:假设神经网络的第一层的权重矩阵为θW∈Rd×kθW∈Rd×k, 偏置向量为...