New activation functions for single layer feedforward neural networkArtificial Neural NetworkActivation functionGeneralized swishReLU-swishTriple-state swishArtificial Neural Network (ANN) is a subfield of machine learning and it has been widely used by the researchers. The attractiveness of ANNs comes ...
According to conventional neural network theories, the feature of single-hidden-layer feedforward neural networks(SLFNs) resorts to parameters of the weighted connections and hidden nodes. SLFNs are universal approximators when at least the parameters of the networks including hidden-node parameter and...
In this chapter, two major issues are addressed: (i) how to obtain a more compact network architecture and (ii) how to reduce the overall computational complexity. An integrated analytic framework is introduced for the fast construction of single-hidden-layer feedforward networks (SLFNs) with ...
Extreme learning machine Single-hidden layer feedforward neural network Adaptive hidden nodes initialization Automated cancer detection Microarray Mass spectrometry 1. Introduction Gene expression arrays offer an effective solution for analyzing expression of known genes and transcripts, thus providing valuable ...
In this paper, we propose a multi-criteria decision making based architecture selection algorithm for single-hidden layer feedforward neural networks trained by extreme learning machine. Two criteria are incorporated into the selection process, i.e., training accuracy and the Q-value estimated by ...
Create and configure a network. [x,t] = simplefit_dataset; net = feedforwardnet(3); net = configure(net,x,t); view(net) This network has three weights and three biases in the first layer, and three weights and one bias in the second layer. So, the total number of weight and bias...
Description formwb(net,b,IW,LW) takes a neural network and bias b, input weight IW, and layer weight LW values, and combines the values into a single vector. Examples Here a network is created, configured, and its weights and biases formed into a vector....
Create and configure a network. [x,t] = simplefit_dataset; net = feedforwardnet(3); net = configure(net,x,t); view(net) This network has three weights and three biases in the first layer, and three weights and one bias in the second layer. So, the total number of weight and bias...
of a matrix protein (M1) layer surrounded by a lipid membrane. The exact role of M1 protein and its mode of interaction with the viral membrane are unknown. We have set out to investigate the mechanical design of influenza virus: we im- ...
Gopal S, Fischer M M, 1996, "Learning in single hidden-layer feedforward network models" Geographical Analysis 28 (1) 38 - 55Gopal, S. and Fischer, M.M. 1996. Learning in single hidden-layer feedforward network models: Backpropagation in a spatial interaction modelling context. Geographical...