Application of functional link neural network to HVAC thermal dynamic system identification

Teeter, J.; Chow, Mo-Yuen

doi:10.1109/41.661318

Cited by 84 publications

(31 citation statements)

References 13 publications

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“…The functional link acts on an element of an input vector, or on all the input vectors, by generating a set of linearly independent functions, then evaluating these functions with the pattern as the argument. Thus, both the training time and training error of the network can be improved [113]. Fig.…”

Section: Flnsmentioning

confidence: 99%

“…The use of an NN provided a means of adapting a controller online, in an effort to minimize a given cost index. The identification networks demonstrated the capacity to learn changes in the plant dynamics and to accurately predict future plan behavior [113]. A robust ANN controller, to the motion control of rigid-link electrically driven robot using an FLN, had been presented.…”

Section: Flnsmentioning

confidence: 99%

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A comprehensive review for industrial applicability of artificial neural networks

Meireles

Almeida

Simões

2003

IEEE Trans. Ind. Electron.

393

136

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Abstract-This paper presents a comprehensive review of the industrial applications of artificial neural networks (ANNs), in the last 12 years. Common questions that arise to practitioners and control engineers while deciding how to use NNs for specific industrial tasks are answered. Workable issues regarding implementation details, training and performance evaluation of such algorithms are also discussed, based on a judiciously chronological organization of topologies and training methods effectively used in the past years. The most popular ANN topologies and training methods are listed and briefly discussed, as a reference to the application engineer. Finally, ANN industrial applications are grouped and tabulated by their main functions and what they actually performed on the referenced papers. The authors prepared this paper bearing in mind that an organized and normalized review would be suitable to help industrial managing and operational personnel decide which kind of ANN topology and training method would be adequate for their specific problems.

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Section: Flnsmentioning

confidence: 99%

Section: Flnsmentioning

confidence: 99%

A comprehensive review for industrial applicability of artificial neural networks

Meireles

Almeida

Simões

2003

IEEE Trans. Ind. Electron.

393

136

View full text Add to dashboard Cite

show abstract

“…Functional Link Neural Network is a class of HONNs created by Pao [7] and has been successfully used in many applications such as system identification [9][10][11][12][13][14], channel equalization [3], classification [15][16][17][18], pattern recognition [19,20] and prediction [21,22]. In this paper, we would discuss on the FLNN for the prediction task.…”

Section: Functional Link Neural Networkmentioning

confidence: 99%

Functional Link Neural Network – Artificial Bee Colony for Time Series Temperature Prediction

Hassim

Ghazali

2013

Lecture Notes in Computer Science

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Abstract. Higher Order Neural Networks (HONNs) have emerged as an important tool for time series prediction and have been successfully applied in many engineering and scientific problems. One of the models in HONNs is a Functional Link Neural Network (FLNN) known to be conveniently used for function approximation and can be extended for pattern recognition with faster convergence rate and lesser computational load compared to ordinary feedforward network like the Multilayer Perceptron (MLP). In training the FLNN, the mostly used algorithm is the Backpropagation (BP) learning algorithm. However, one of the crucial problems with BP learning algorithm is that it can be easily gets trapped on local minima. This paper proposed an alternative learning scheme for the FLNN to be applied on temperature forecasting by using Artificial Bee Colony (ABC) optimization algorithm. The ABC adopted in this work is known to have good exploration and exploitation capabilities in searching optimal weight especially in numerical optimization problems. The result of the prediction made by FLNN-ABC is compared with the original FLNN architecture and toward the end we found that FLNN-ABC gives better result in predicting the next-day ahead prediction.

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“…The neural network is placed in parallel with the system and the error e between the system outputs y and network outputs y is used to train the network. This represents a classical supervised learning problem for which the teacher (i.e., the system) provides target values (i.e., system outputs) directly in the output coordinate system of the learner (i.e., the network model) [29] .…”

Section: Intelligent Controllers: This Category Of Controllers Includmentioning

confidence: 99%

Control Techniques in Heating, Ventilating and Air Conditioning (HVAC) Systems

Mirinejad¹,

Sadati²,

Ghasemian³

et al. 2008

J. of Computer Science

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Problem statement: Heating, Ventilating and Air Conditioning (HVAC) systems are among the main installations in residential, commercial and industrial buildings. The purpose of the HVAC systems is normally to provide a comfortable environment in terms of temperature, humidity and other environmental parameters for the occupants as well as to save energy. Achieving these objectives requires a suitable control system design. Approach: In this overview, thermal comfort level and ISO comfort field is introduced, followed by a review and comparison of the main existing control techniques used in HVAC systems to date. Results: The present overview shows that intelligent controllers which are based on the human sensation of thermal comfort have a better performance in providing thermal comfort as well as energy saving than the traditional controllers and those based on a model of the HVAC system. Conclusion: Such an overview provides an insight into current control methods in HVAC systems and can help scholars and HVAC learners to have the comprehensive information about a variety of control techniques in the field of HVAC and therefore to better design a proper controller for their work.

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Application of functional link neural network to HVAC thermal dynamic system identification

Cited by 84 publications

References 13 publications

A comprehensive review for industrial applicability of artificial neural networks

A comprehensive review for industrial applicability of artificial neural networks

Functional Link Neural Network – Artificial Bee Colony for Time Series Temperature Prediction

Control Techniques in Heating, Ventilating and Air Conditioning (HVAC) Systems

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