• Proceedings of the KIPE Conference
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• 2019.11a
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• pp.174-175
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• 2019

Battery cells are connected in parallel to enlarge the system capacity. However, cell inconsistency may reduce the overall system capacity and cause the over-charging or over-discharging issue. This paper proposes a SOC-based sequencing equalizer for parallel-connected battery configuration that uses the ANFIS (adaptive neuro-fuzzy inference system) algorithm to make the switching decision. Depend on the load current and the SOC (state-of-charge) rate of cells, the switching decision is made to equalize the SOC of the battery cells. The simulation results show that the system capacity is maximized and the controller is adaptive for a large number of parallel-connected in dynamic load profile.

• International journal of advanced smart convergence
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• v.3 no.2
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• pp.18-21
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• 2014

A control application requirements currently used is very low, such as packet loss rate, minimum delay on sensor networks with quality of service (QoS) requirements some packet delivery guarantee. This paper is the sampling period at the end of the actuator and sensor data transfer related to the Miss ratio for each source sensor node, use the controller and the internal ANFIS. The proposed scheme has the advantages of simplicity, scalability, and General. Simulation results of the proposed scheme can provide QoS support in WSANs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.2 no.2
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• pp.223-230
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• 1998

This paper describes a phase-shift pulse-width modulation and pulse-frequency modulation series resonant high-frequency inverter using IGBT(Insulated-Gated Bipolar Transistor) for the power control of high-frequency induction heating using neuro-fuzzy, which is practically applied for 20KHz~500KHz induction-heating and melting power supply in industrial fields. The adaptive frequency tracking based phase-shifting PWM(Pulse-Width Modulation) regulation scheme is presented in order to minimize switching losses. The trially-produced breadboards using IGBT are successfully demonstrated and discussed.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.12
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• pp.85-98
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• 1998

This paper proposes a new design method of neuro-FLC by the Lamarckian co-adaptation scheme that incorporates the backpropagation learning into the GA evolution in an attempt to find optimal design parameters (fuzzy rule base and membership functions) of application-specific FLC. The design parameters are determined by evolution and learning in a way that the evolution performs the global search and makes inter-FLC parameter adjustments in order to obtain both the optimal rule base having high covering value and small number of useful fuzzy rules and the optimal membership functions having small approximation error and good control performance while the learning performs the local search and makes intra-FLC parameter adjustments by interacting each FLC with its environment. The proposed co-adaptive design method produces better approximation ability because it includes the backpropagation learning in every generation of GA evolution, shows better control performance because the used COG defuzzifier computes the crisp value accurately, and requires small workspace because the optimization procedure of fuzzy rule base and membership functions is performed concurrently by an integrated fitness function on the same fuzzy partition. Simulation results show that the Lamarckian co-adapted FLC produces the most superior one among the differently generated FLCs in all aspects such as the number of fuzzy rules, the approximation ability, and the control performance.