Anti-swing control of the overhead crane system based on the harmony search radial basis function neural network algorithm

The swing of the grab is a main factor affecting the working efficiency of overhead cranes. Thus, planning the optimal motion path can reduce the adverse effects caused by the grab swing and improve the loading and unloading efficiency. The dynamic model of the trolley–grab system is established by considering factors like the change of rope length, wind load, and air resistance. First, the radial basis function neural network is applied to generate a feasible motion trajectory of the crane trolley. Taking the swing angle and angular velocity of the grab at the discharge point as evaluation, the harmony search algorithm is then applied to optimize the neural network parameters and obtain the optimal anti-swing motion trajectory. The numerical simulation and practical testing results show that the harmony search–radial basis function algorithm generates a smooth motion trajectory with good convergence, achieving anti-swing control of the trolley–grab system.

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Designing a New Radial Basis Function Neural Network by Harmony Search for Diabetes Diagnosis

Optical Memory and Neural Networks ◽

10.3103/s1060992x19040088 ◽

2019 ◽

Vol 28 (4) ◽

pp. 321-331 ◽

Cited By ~ 1

Author(s):

Davar Giveki ◽

Homayoun Rastegar

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Basis Function ◽

Harmony Search ◽

Diabetes Diagnosis ◽

Radial Basis

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K-Means-Based Polynomial-Radial Basis Function Neural Network Using Space Search Algorithm: Design and Comparative Studies

Journal of Institute of Control Robotics and Systems ◽

10.5302/j.icros.2011.17.8.731 ◽

2011 ◽

Vol 17 (8) ◽

pp. 731-738

Author(s):

Wook-Dong Kim ◽

Sung-Kwun Oh

Keyword(s):

Neural Network ◽

Radial Basis Function ◽

Basis Function ◽

Comparative Studies ◽

Search Algorithm ◽

Algorithm Design ◽

Radial Basis

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Hybridization of Harmonic Search Algorithm In Training Radial Basis Function With Dynamic Decay Adjustment For Condition Monitoring

10.21203/rs.3.rs-174329/v1 ◽

2021 ◽

Author(s):

Hue Yee CHONG ◽

Shing Chiang TAN ◽

Hwa Jen Yap

Keyword(s):

Radial Basis Function ◽

Condition Monitoring ◽

Basis Function ◽

Intelligent System ◽

Search Algorithm ◽

Radial Basis Function Network ◽

Harmony Search ◽

New Information ◽

Radial Basis ◽

Dynamic Decay Adjustment

Abstract In recent decades, hybridization of superior attributes of few algorithms was proposed to aid in covering more areas of complex application as well as improves performance. This paper presents an intelligent system integrating a Radial Basis Function Network with Dynamic Decay Adjustment (RBFN-DDA) with a Harmony Search (HS) to perform condition monitoring in industrial processes. An effective condition monitoring can help reduce unexpected breakdown incidents and facilitate in maintenance. RBDN-DDA performs incremental learning wherein its structure expands by adding new hidden units to include new information. As such, its training can reach stability in a shorter time compared to the gradient-descent based methods. By integrating with the HS algorithm, the proposed metaheuristic neural network (RBFN-DDA-HS) can optimize the RBFN-DDA parameters and improve classification performances from the original RBFN-DDA by 2.2% up to 22.5% in two benchmarks and a real-world condition-monitoring case studies. The results also show that the proposed RBFN-DDA-HS is compatible, if not better than, the classification performances of other state-of-art machine learning methods.

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