Computational intelligence techniques have been used in wide applications. Out of numerous computational intelligence techniques, neural networks and support vector machines (SVMs) have been playing the dominant roles. However, it is known that both neural networks and SVMs face some challenging issues such as:(1) slow learning speed, (2) trivial human intervene, and/or (3) poor computational scalability. Extreme learning machine (ELM) as emergent technology which overcomes some challenges faced by other techniques has recently attracted the attention from more and more researchers. ELM works for generalized single-hidden layer feedforward networks (SLFNs). The essence of ELM is that the hidden layer of SLFNs need not be tuned. Compared with those traditional computational intelligence techniques, ELM provides better generalization performance at a much faster learning speed and with least human intervene. This paper gives a survey on ELM and its variants, especially on (1) batch learning mode of ELM, (2) fully complex ELM, (3) online sequential ELM, (4) incremental ELM, and (5) ensemble of ELM.

A new hybrid self-adaptive training approach-based radial basis function (RBF) neural network for power transformer fault diagnosis is presented in this paper. The proposed method is able to generate RBF neural network models based on fuzzy c-means (FCM) and quantum-inspired particle swarm optimization (QPSO), which can automatically configure network structure and obtain model parameters. With these methods, the number of neuron, centers and radii of hidden layer activated functions, as well as output connection weights can be automatically calculated. This learning method is proved to be effective by applying the RBF neural network in the classification of five benchmark testing data sets, and power transformer fault data set. The results clearly demonstrated the improved classification accuracy compared with other alternatives and showed that it can be used as a reliable tool for power transformer fault analysis.Index Terms-Computational methods, particle swarm optimization, power transformer fault diagnosis, radial basis function (RBF) neural network.

The RTOS (Real-Time Operating System) is a critical component in the SoC (System-on-a-Chip), which is the main body for consuming total system energy. Power optimization based on hardware-software partitioning of a RTOS (RTOS-Power partitioning) can significantly minimize the energy consumption of a SoC. This paper presents a new model for RTOS-Power partitioning, which helps in understanding the essence of the RTOS-Power partitioning techniques. A discrete Hopfield neural network approach for implementing the RTOS-Power partitioning is proposed, where a novel energy function, operating equation and coefficients of the neural network are redefined. Simulations are carried out with comparison to other optimization techniques. Experimental results demonstrate that the proposed method can achieve higher energy savings up to 60% at relatively low costs of less than 4k PLBs while increasing the performance compared to the purely software realized SoC-RTOS.

This paper aims to develop a state estimate-based friction fuzzy modeling and robust adaptive control techniques for controlling a class of multiple degrees of freedom (MDOF) mechanical systems. A fuzzy state estimator is proposed to estimate the state variables for friction modeling. Under some conditions, it is shown that such a state estimator guarantees the uniformly ultimate boundedness (UUB) of the estimate error. Based on system input–output data and our proposed state estimator, a robust adaptive fuzzy output-feedback control scheme is presented to control multiple degrees of freedom system with friction. The adaptive fuzzy output-feedback controller can guarantee the uniformly ultimate boundedness of the tracking error of the closed-loop system. A typical mass-spring system is employed in our simulation studies. The results demonstrate that our proposed techniques in this paper have good potential in controlling nonlinear systems with uncertain friction.