Miin-Tsair Su

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Video-based fire detection is currently a fairly common application with the growth in the number of installed surveillance video systems. Moreover, the related processing units are becoming more powerful. Smoke is an early sign of most fires; therefore, selecting an appropriate smoke-detection method is essential. However, detecting smoke without creating(More)
This study presents an evolutionary neural fuzzy system (NFS) for nonlinear system control. The proposed NFS model uses functional link neural networks (FLNNs) as the consequent part of the fuzzy rules. This study uses orthogonal polynomials and linearly independent functions in a functional expansion of the functional link neural networks. A learning(More)
This study proposes a Rule-Based Symbiotic MOdified Differential Evolution (RSMODE) for SelfOrganizing Neuro-Fuzzy Systems (SONFS). The RSMODE adopts a multi-subpopulation scheme that uses each individual represents a single fuzzy rule and each individual in each subpopulation evolves separately. The proposed RSMODE learning algorithm consists of structure(More)
1 Abstract algorithm to reach the local minima very fast, but never finds a global solution. In addition, BP training performance depends on the initial system parameter values. For different network topologies one must derive new mathematical expressions for each network layer. In order to enhance the immune algorithm (IA) performance and find the optimal(More)
This study presents a new evolutionary learning algorithm to optimize the parameters of the neural fuzzy classifier (NFC). This new evolutionary learning algorithm is based on a hybrid of bacterial foraging optimization and particle swarm optimization. It is thus called bacterial foraging particle swarm optimization (BFPSO). The proposed BFPSO method(More)
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