Modeling and Simulation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) for Mobile Learning

Al-Hmouz, Ahmed; Shen, Jun; Al‐Hmouz, Rami; Yan, Jun

doi:10.1109/tlt.2011.36

Cited by 236 publications

(99 citation statements)

References 13 publications

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“…Broadly, ANN and FLC suffer from some reported drawbacks [27][28][29][30][31][32][33][34]. However, the cooperative of neuro-fuzzy algorithms can be used to overcome the deficiencies of the ANN and FLC methods [35].…”

Section: Summary and Shortcomings Of The Reviewmentioning

confidence: 99%

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An Application of a Novel Technique for Assessing the Operating Performance of Existing Cooling Systems on a University Campus

et al. 2018

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Abstract:Optimal operation is an important aspect of energy efficiency that can be employed to reduce power consumption. In cooling systems, the chillers consume a large amount of electricity, especially if they are not optimally operated, therefore, they cannot produce the required or rated cooling load capacity. The objective of this paper is to improve coefficient of performance (COP) for the operation of chillers and to reduce power consumption. Two contributions in this work are: (1) the prediction of a model by using Adaptive Neuro-Fuzzy Inference System (ANFIS)-based Fuzzy Clustering Subtractive (FCS), and (2) the classification and optimization of the predicted models by using an Accelerated Particle Swarm Optimization (APSO) algorithm. Particularly, in contribution (1), two models are developed to predict/assess power consumption and cooling load capacity. While in contribution (2), the predictive model's data obtained are used to classify the operating performance of the chiller and to optimize the model in order to reduce power consumption and cooling capacity. Therefore, data classification by APSO is used to enhance the coefficient of performance (COP). The proposed technique reduces the total power consumption by 33.2% and meets the cooling demand requirements. Also, it improves the cooling performance based on COP, thus resulting in a 15.95% increase in efficiency compared to the existing cooling system. The studied ANFIS-based FCS outperforms the ANFIS-based fuzzy C-means clustering in terms of the regression. Then, the algorithm-based classifier APSO has better results compared to the conventional particle swarm optimization (PSO). The data was acquired from the District Cooling System (DCS) at the Universiti Teknologi Petronas (UTP) campus in Malaysia.

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Section: Summary and Shortcomings Of The Reviewmentioning

confidence: 99%

“…The ANN and FIS have good capabilities and interpretability for learning methods and both are used as expert systems [31]. The combination of these two techniques overcome the limitations of ANN computational time and FIS rules error [27][28][29][30][31].…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

An Application of a Novel Technique for Assessing the Operating Performance of Existing Cooling Systems on a University Campus

et al. 2018

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“…Then as presented in [19], an adaptive neuro-fuzzy inference system (ANFIS), which integrates neural network with fuzzy inference system, was deployed to control the speed of a heavy duty vehicle. Not only for control system, but also for mobile learning system, [20,21] incorporated ANFIS as a reasoning engine to deliver learning content for mobile learning system.…”

Section: Fuzzy Logic and Evolving Fuzzy Neural Network (Efunn)mentioning

confidence: 99%

Effective long-term travel time prediction with fuzzy rules for tollway

Li¹,

Rose

Chen

et al. 2017

Neural Comput & Applic

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Abstract:Advanced traveller information system is an important Intelligent Transportation Systems (ITS) application area, which provides information to transport users and managers in order to improve the efficiency and effectiveness of the transportation system, in the face of increasing congestion in urban cities around the world. So far very limited research attention has been focused on long term travel time prediction (i.e. predicting greater than 60 minutes ahead). Long term travel time forecasts can play a critical role in journey planning decisions for both private road users and logistics operators. In this paper, we have considered a fuzzy neural network incorporated with both imprecise and numerical information and developed a hybrid long term travel time prediction model, which shows the better prediction capability than naive methods and highlights the importance of different data variables. The model combines the learning ability of neural networks and the knowledge extraction ability of fuzzy inference systems. The model was validated by using travel time data compiled from electronic toll tags on a 14 km length section of the CityLink tollway in Melbourne, Australia. The validation results highlight the ability of the fuzzy neural network model to accommodate imprecise and linguistic input information, while providing reliable predictions of travel times up to a few days ahead.

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“…Có nhiều mô hình dự báo đã được công bố trong thời gian gần đây như sử dụng cấu trúc cây TAN (Tree Augmented Naïve Bayes) [15], mạng neural ANN [3,14,21], mô hình SVM [6,28],… Trong các mô hình dự báo, hệ ANFIS phù hợp cho các bài toán có dữ liệu đầu vào phức tạp và dự báo được đồng thời nhiều kết quả khác nhau, ví dụ như dự báo giá cổ phiếu dựa trên Hamacher T-Norm nhiều đầu vào và ANFIS [29], dự báo giá đóng (close) [25], mô hình ANFIS áp dụng trên di động [2].…”

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