Adaptive Learning of Probabilistic Neural Network in Situation of Overlapping Classes in Classification Task

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code. Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

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A Hybrid Approach for Intrusion Detection using OPSO and Hybridization of Feed Forward Neural Network (FFNN) with Probabilistic Neural Network (PNN)- HFFPNN Classifier

International Journal of Advanced Trends in Computer Science and Engineering ◽

10.30534/ijatcse/2020/31912020 ◽

2020 ◽

Vol 9 (1) ◽

pp. 206-210

Author(s):

Sangita Babu

Keyword(s):

Neural Network ◽

Intrusion Detection ◽

Probabilistic Neural Network ◽

Hybrid Approach ◽

Feed Forward Neural Network ◽

Feed Forward

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The Classification of Power Quality Disturbances using Statistical S-Transform and Probabilistic Neural Network

2021 1st Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology(ODICON) ◽

10.1109/odicon50556.2021.9428947 ◽

2021 ◽

Author(s):

Laxmipriya Samal ◽

Hemanta Kumar Palo ◽

Badri Narayan Sahu ◽

Debashisa Samal

Keyword(s):

Neural Network ◽

Power Quality ◽

Probabilistic Neural Network ◽

S Transform ◽

Power Quality Disturbances

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Influence of hinge length and distribution number on the camber and cornering properties of a non-pneumatic tire

Advances in Mechanical Engineering ◽

10.1177/1687814020984687 ◽

2020 ◽

Vol 12 (12) ◽

pp. 168781402098468

Author(s):

Xianbin Du ◽

Youqun Zhao ◽

Yijiang Ma ◽

Hongxun Fu

Keyword(s):

Neural Network ◽

Network Model ◽

Adaptive Learning ◽

High Speed ◽

Back Propagation ◽

Lateral Force ◽

Learning Ability ◽

Vehicle Performance ◽

Neural Network Theory ◽

Bp Neural Network Model

The camber and cornering properties of the tire directly affect the handling stability of vehicles, especially in emergencies such as high-speed cornering and obstacle avoidance. The structural and load-bearing mode of non-pneumatic mechanical elastic (ME) wheel determine that the mechanical properties of ME wheel will change when different combinations of hinge length and distribution number are adopted. The camber and cornering properties of ME wheel with different hinge lengths and distributions were studied by combining finite element method (FEM) with neural network theory. A ME wheel back propagation (BP) neural network model was established, and the additional momentum method and adaptive learning rate method were utilized to improve BP algorithm. The learning ability and generalization ability of the network model were verified by comparing the output values with the actual input values. The camber and cornering properties of ME wheel were analyzed when the hinge length and distribution changed. The results showed the variation of lateral force and aligning torque of different wheel structures under the combined conditions, and also provided guidance for the matching of wheel and vehicle performance.

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