Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
Universal learning network and its application to chaos control
Neural Networks
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
Fast learning in networks of locally-tuned processing units
Neural Computation
A new control method of nonlinear systems based on impulseresponses of universal learning networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Effect of probabilistic inputs on neural network-based electric load forecasting
IEEE Transactions on Neural Networks
Computing second derivatives in feed-forward networks: a review
IEEE Transactions on Neural Networks
pth Moment Exponential Stability of Stochastic Recurrent Neural Networks with Markovian Switching
Neural Processing Letters
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The way of propagating and control of stochastic signals through Universal Learning Networks (ULNs) and its applications are proposed. ULNs have been already developed to form a superset of neural networks and have been applied as a universal framework for modeling and control of non-linear large-scale complex systems. However, the ULNs cannot deal with stochastic variables. Deterministic signals can be propagated through a ULN, but the ULN does not provide any stochastic characteristics of the signals propagating through it. The proposed method named Probabilistic Universal Learning Networks (PrULNs) can process stochastic variables and can train network parameters so that the signals behave with the pre-specified stochastic properties. As examples of applications of the proposed method, control and identification of non-linear dynamic systems with noises are studied, and it is shown that the method are useful for dealing with the control and identification of the non-linear stochastic systems contaminated with noises.