Neuro-fuzzy controller of low head hydropower plants using adaptive-network based fuzzy inference system

Djukanović, M.; Ćalović, M.S.; Vesovic, B.V.; Šobajić, D.J.

doi:10.1109/60.638941

Cited by 46 publications

(20 citation statements)

References 15 publications

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“…Djukanovic et al, 1997;Altug et al, 1999). This neuro-fuzzy network is a five-layer feedforward network that uses neural network learning algorithms coupled with fuzzy reasoning to map an input space to an output space.…”

Section: The Anfismentioning

confidence: 99%

Intelligent control for modeling of real-time reservoir operation, part II: artificial neural network with operating rule curves

Chang

2005

Hydrol. Process.

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Abstract:To bridge the gap between academic research and actual operation, we propose an intelligent control system for reservoir operation. The methodology includes two major processes, the knowledge acquired and implemented, and the inference system. In this study, a genetic algorithm (GA) and a fuzzy rule base (FRB) are used to extract knowledge based on the historical inflow data with a design objective function and on the operating rule curves respectively. The adaptive network-based fuzzy inference system (ANFIS) is then used to implement the knowledge, to create the fuzzy inference system, and then to estimate the optimal reservoir operation. To investigate its applicability and practicability, the Shihmen reservoir, Taiwan, is used as a case study. For the purpose of comparison, a simulation of the currently used M-5 operating rule curve is also performed. The results demonstrate that (1) the GA is an efficient way to search the optimal input-output patterns, (2) the FRB can extract the knowledge from the operating rule curves, and (3) the ANFIS models built on different types of knowledge can produce much better performance than the traditional M-5 curves in real-time reservoir operation. Moreover, we show that the model can be more intelligent for reservoir operation if more information (or knowledge) is involved.

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Section: The Anfismentioning

confidence: 99%

Intelligent control for modeling of real-time reservoir operation, part II: artificial neural network with operating rule curves

Chang

2005

Hydrol. Process.

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“…With the ability to combine the verbal power of a fuzzy system with the numeric power of a neural system adaptive network, ANFIS has been shown to be powerful in modelling numerous processes, e.g. motor fault detection and diagnosis (Altug et al, 1999), power systems dynamic load (Djukanovic et al, 1997;Oonsivilai and El-Hawary, 1999), and real-time reservoir operation (Chang and Chang, 2001;Chang et al, 2005).…”

Section: The Anfismentioning

confidence: 99%

The strategy of building a flood forecast model by neuro‐fuzzy network

Chen

Lin

Chang

et al. 2005

Hydrological Processes

119

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Abstract:A methodology is proposed for constructing a flood forecast model using the adaptive neuro-fuzzy inference system (ANFIS). This is based on a self-organizing rule-base generator, a feedforward network, and fuzzy control arithmetic. Given the rainfall-runoff patterns, ANFIS could systematically and effectively construct flood forecast models. The precipitation and flow data sets of the Choshui River in central Taiwan are analysed to identify the useful input variables and then the forecasting model can be self-constructed through ANFIS. The analysis results suggest that the persistent effect and upstream flow information are the key effects for modelling the flood forecast, and the watershed's average rainfall provides further information and enhances the accuracy of the model performance. For the purpose of comparison, the commonly used back-propagation neural network (BPNN) is also examined. The forecast results demonstrate that ANFIS is superior to the BPNN, and ANFIS can effectively and reliably construct an accurate flood forecast model.

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“…With the ability to combine the verbal power of a fuzzy system with the numeric power of a neural system adaptive network, ANFIS has been shown to be powerful in modeling numerous processes, such as motor fault detection and diagnosis (Altug, Chow & Trussell , 1999), power systems dynamic load (Djukanovic et al, 1997) , (Oonsivilai & El-Hawary, 1999), wind speed (Sfetsos. 2000), forecasting system for the demand of teaching human resources (Liao, Su & Wu, 2001), and real time reservoir operation (Chang et al, 2005).…”

Section: Architecture and Algorithmmentioning

confidence: 99%