This article is concerned with the issue of quantized sliding-mode control (SMC) design methodology for nonlinear stochastic switching systems subject to semi-Markovian switching parameters, T-S fuzzy strategy, uncertainty, signal quantization, and nonlinearity. Compared with the previous literature, the quantized control input is first considered in studying T-S fuzzy stochastic switching systems with a semi-Markovian process. A mode-independent sliding surface is adopted to avoid the potential repetitive jumping effects. Then, by means of the Lyapunov function, stochastic stability criteria are proposed to be dependent of sojourn time for the corresponding slidingmode dynamics. Furthermore, the fuzzy-model-based SMC law is proposed to ensure the finite-time reachability of the sliding-mode dynamics. Finally, an application example of a modified series dc motor model is provided to demonstrate the effectiveness of the theoretical findings.

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This paper explores feasibility of employing the non-recurrent backpropagation training algorithm for a recurrent neural network, Simultaneous Recurrent Neural network, for static optimisation. A simplifying observation that maps the recurrent network dynamics, which is configured to operate in relaxation mode as a static optimizer, to feedforward network dynamics is leveraged to facilitate application of a non-recurrent training algorithm such as the standard backpropagation and its variants. A simulation study that aims to assess feasibility, optimizing potential, and computational efficiency of training the Simultaneous Recurrent Neural network with non-recurrent backpropagation is conducted. A comparative computational complexity analysis between the Simultaneous Recurrent Neural network trained with non-recurrent backpropagation algorithm and the same network trained with the recurrent backpropagation algorithm is performed. Simulation results demonstrate that it is feasible to apply the non-recurrent backpropagation to train the Simultaneous Recurrent Neural network. The optimality and computational complexity analysis fails to demonstrate any advantage on behalf of the non-recurrent backpropagation versus the recurrent backpropagation for the optimisation problem considered. However, considerable future potential that is yet to be explored exists given that computationally efficient versions of the backpropagation training algorithm, namely quasiNewton and conjugate gradient descent among others, are also applicable for the neural network proposed for static optimisation in this paper.

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