Neural-network solutions to stochastic reaction networks

Stochastic Neural Networks and Their Solutions to Optimisation Problems

Stochastic neural networks which are a type of recurrent neural networks can be basicly and simply expressed as “the neural networks which are built by introducing random variations into the network”. This randomness comes from one of these usages : applying stochastic transfer functions to network neurons or determining the network weights stochastically. This randomness property makes this ty...

Training Radial Basis Function Neural Network using Stochastic Fractal Search Algorithm to Classify Sonar Dataset

Radial Basis Function Neural Networks (RBF NNs) are one of the most applicable NNs in the classification of real targets. Despite the use of recursive methods and gradient descent for training RBF NNs, classification improper accuracy, failing to local minimum and low-convergence speed are defects of this type of network. In order to overcome these defects, heuristic and meta-heuristic algorith...

Stochastic neural network models for gene regulatory networks

Recent advances in gene-expression profiling technologies provide large amounts of gene expression data. This raises the possibility for a functional understanding of genome dynamics by means of mathematical modelling. As gene expression involves intrinsic noise, stochastic models are essential for better descriptions of gene regulatory networks. However, stochastic modelling for large scale ge...

Stochastic delayed neural networks

Stochastic Hopfield neural networks

Hopfield (1984 Proc. Natl Acad. Sci. USA 81 3088–92) showed that the time evolution of a symmetric neural network is a motion in state space that seeks out minima in the system energy (i.e. the limit set of the system). In practice, a neural network is often subject to environmental noise. It is therefore useful and interesting to find out whether the system still approaches some limit set unde...