Radial Basis Function (RBF) Neural Network Control for Mechanical Systems

Liu, Jinkun

doi:10.1007/978-3-642-34816-7

Cited by 173 publications

(100 citation statements)

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“…Another type of ANN that has been used in the literature is the radial basis functions neural network (RBFNN) [12]. A RBFNN can approximate any function which makes it suitable to model the relationship between inputs (the various cost drivers) and output (effort required).…”

Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

“…The following generic description of a RBF neural network is based on a tutorial given in [11] [12]. Figure 1 describes a typical architecture of an RBFNN.…”

Section: Rbfnn Implementationmentioning

confidence: 99%

“…Again, in this context, the value of  is not in itself important and what is needed is some coefficient that controls the width of the bell curve. Thus, after making these two modifications, the RBFNN neuron activation function can be written as in equation 2 [12]:…”

Section: Rbfnn Implementationmentioning

confidence: 99%

“…Every RBF neuron in the network will have some influence over the classification decision. The exponential fall off of the activation function, however, means that the neurons whose prototypes are far from the input vector will actually contribute very little to the result [12]. …”

Section: Rbfnn Implementationmentioning

confidence: 99%

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Software Defect Prediction Using Radial Basis and Probabilistic Neural Networks

Mehdi¹

2016

IJCATR

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Defects in modules of software systems is a major problem in software development. There are a variety of data mining techniques used to predict software defects such as regression, association rules, clustering, and classification. This paper is concerned with classification based software defect prediction. This paper investigates the effectiveness of using a radial basis function neural network and a probabilistic neural network on prediction accuracy and defect prediction. The conclusions to be drawn from this work is that the neural networks used in here provide an acceptable level of accuracy but a poor defect prediction ability. Probabilistic neural networks perform consistently better with respect to the two performance measures used across all datasets. It may be advisable to use a range of software defect prediction models to complement each other rather than relying on a single technique.

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Section: Radial Basis Function Neural Networkmentioning

confidence: 99%

“…The following generic description of a RBF neural network is based on a tutorial given in [11] [12]. Figure 1 describes a typical architecture of an RBFNN.…”

Section: Rbfnn Implementationmentioning

confidence: 99%

Section: Rbfnn Implementationmentioning

confidence: 99%

Section: Rbfnn Implementationmentioning

confidence: 99%

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Software Defect Prediction Using Radial Basis and Probabilistic Neural Networks

Mehdi¹

2016

IJCATR

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“…This renders the neural networks suitable for system identification and control applications [19][20][21]. Although intelligent and hybrid algorithms are already being implemented in the domains of image processing, robotics, financial management, and so on, their application in the field of FACTS devices for power flow control is fairly recent.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid UPFC Controller for Power Flow Control and Voltage Regulation Based on RBF Neurosliding Mode Technique

Kenné

Kuate

Fombu

et al. 2017

Advances in Electrical Engineering

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This paper presents a new technique to design a Unified Power Flow Controller (UPFC) for power flow control and DC voltage regulation of an electric power transmission system which is based on a hybrid technique which combines a Radial Basis Function (RBF) neural network (online training) with the sliding mode technique to take advantage of their common features. The proposed controller does not need the knowledge of the perturbation bounds nor the full state of the nonlinear system. Hence, it is robust and produces an optimal response in the presence of system parameter uncertainty and disturbances. The performance of the proposed controller is evaluated through numerical simulations on a Kundur power system and compared with a classical PI controller. Simulation results confirm the effectiveness, robustness, and superiority of the proposed controller.

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Robust RBF neural network–based backstepping controller for implantable cardiac pacemakers

Karar

2018

Adaptive Control & Signal

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Implantable cardiac pacemaker is a standard medical device to treat heart rhythm disorders. In this paper, a new adaptive backstepping controller is developed to enhance the performance of dual-sensor pacemakers for regulating the heart rate based on radial basis function neural networks. The robust design of adaptive backstepping controller using Lyapunov functions allows to guarantee the stability and performance of the rate-adaptive pacing system for accurately accomplishing the heart rate regulation at different preset or desired values. The developed control system has been successfully validated using 12 cases of the preset heart rates for 4 patients during 3 body activities, namely, at rest, walking, and jogging. The resulting root mean square error and maximum error are less than 0.9 and 1.7%, respectively. Moreover, the comparative results of this study showed that the performance of developed backstepping controller is superior to other pacemaker controllers in the previous studies. Therefore, it is potentially valid to be applied in dual-sensor cardiac pacemakers for the clinical use. KEYWORDSbackstepping control, dual-sensor pacemaker, intelligent control, Lyapunov stability analysis, neural networks, radial basis function

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Radial Basis Function (RBF) Neural Network Control for Mechanical Systems

Cited by 173 publications

References 0 publications

Software Defect Prediction Using Radial Basis and Probabilistic Neural Networks

Software Defect Prediction Using Radial Basis and Probabilistic Neural Networks

A New Hybrid UPFC Controller for Power Flow Control and Voltage Regulation Based on RBF Neurosliding Mode Technique

Robust RBF neural network–based backstepping controller for implantable cardiac pacemakers

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