Active control of building structure using lattice probabilistic neural network based on learning algorithm

2012 ◽  
Vol 3 (1/2) ◽  
pp. 75
Author(s):  
Seongkyu Chang ◽  
Dookie Kim
Keyword(s):  
Neural Network ◽  
Active Control ◽  
Learning Algorithm ◽  
Probabilistic Neural Network ◽  
Building Structure

Application of Magnetorheological Damper Semi-Active Control Based on Artificial Neural Networks

2010 ◽  
Vol 171-172 ◽  
pp. 654-658
Author(s):  
De Kun Yue ◽  
Qi Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
System Identification ◽  
Active Control ◽  
Bp Neural Network ◽  
Structural Response ◽  
Magnetorheological Damper ◽  
Building Structure ◽  
Artificial Neural

Uncertainty for the building structure and nonlinear, this simulation of a multi-storey structure under earthquake is presented based on the BP neural network and system identification, controller will be built to effectively reduce the structural response, and to strengthen the unique damper performance.

Active control strategy of structures based on lattice type probabilistic neural network

2008 ◽  
Vol 23 (1) ◽  
pp. 45-50 ◽  
Author(s):  
Dong Hyawn Kim ◽  
Dookie Kim ◽  
Seongkyu Chang ◽  
Hie-Young Jung
Keyword(s):  
Neural Network ◽  
Active Control ◽  
Control Strategy ◽  
Probabilistic Neural Network ◽  
Lattice Type ◽  
Active Control Strategy

scholarly journals Mechanical Fault Diagnosis Using Color Image Recognition of Vibration Spectrogram Based on Quaternion Invariable Moment

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Liang Hua ◽  
Yujian Qiang ◽  
Juping Gu ◽  
Ling Chen ◽  
Xinsong Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Fault Diagnosis ◽  
Extraction Method ◽  
Learning Algorithm ◽  
Probabilistic Neural Network ◽  
Invariant Moment ◽  
Feature Extraction Method ◽  
Time Frequency ◽  
Neural Network Algorithm

Automatic extraction of time-frequency spectral image of mechanical faults can be achieved and faults can be identified consequently when rotating machinery spectral image processing technology is applied to fault diagnosis, which is an advantage. Acquired mechanical vibration signals can be converted into color time-frequency spectrum images by the processing of pseudo Wigner-Ville distribution. Then a feature extraction method based on quaternion invariant moment was proposed, combining image processing technology and multiweight neural network technology. The paper adopted quaternion invariant moment feature extraction method and gray level-gradient cooccurrence matrix feature extraction method and combined them with geometric learning algorithm and probabilistic neural network algorithm, respectively, and compared the recognition rates of rolling bearing faults. The experimental results show that the recognition rates of quaternion invariant moment are higher than gray level-gradient cooccurrence matrix in the same recognition method. The recognition rates of geometric learning algorithm are higher than probabilistic neural network algorithm in the same feature extraction method. So the method based on quaternion invariant moment geometric learning and multiweight neural network is superior. What is more, this algorithm has preferable generalization performance under the condition of fewer samples, and it has practical value and acceptation on the field of fault diagnosis for rotating machinery as well.

scholarly journals Review on Techniques for Plant Leaf Classification and Recognition

Computers ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 77 ◽  
Author(s):  
Muhammad Azfar Firdaus Azlah ◽  
Lee Suan Chua ◽  
Fakhrul Razan Rahmad ◽  
Farah Izana Abdullah ◽  
Sharifah Rafidah Wan Alwi
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Probabilistic Neural Network ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Plant Systematics ◽  
K Nearest Neighbor ◽  
Plant Leaf

Plant systematics can be classified and recognized based on their reproductive system (flowers) and leaf morphology. Neural networks is one of the most popular machine learning algorithms for plant leaf classification. The commonly used neutral networks are artificial neural network (ANN), probabilistic neural network (PNN), convolutional neural network (CNN), k-nearest neighbor (KNN) and support vector machine (SVM), even some studies used combined techniques for accuracy improvement. The utilization of several varying preprocessing techniques, and characteristic parameters in feature extraction appeared to improve the performance of plant leaf classification. The findings of previous studies are critically compared in terms of their accuracy based on the applied neural network techniques. This paper aims to review and analyze the implementation and performance of various methodologies on plant classification. Each technique has its advantages and limitations in leaf pattern recognition. The quality of leaf images plays an important role, and therefore, a reliable source of leaf database must be used to establish the machine learning algorithm prior to leaf recognition and validation.

scholarly journals Development of Seismic Response Simulation Model for Building Structures with Semi-Active Control Devices Using Recurrent Neural Network

2020 ◽  
Vol 10 (11) ◽  
pp. 3915
Author(s):  
Hyun-Su Kim
Keyword(s):  
Neural Network ◽  
Control System ◽  
Seismic Response ◽  
Active Control ◽  
Recurrent Neural Network ◽  
Ground Motions ◽  
Building Structures ◽  
Building Structure ◽  
Control Devices ◽  
Story Building

A structural analysis model to represent the dynamic behavior of building structure is required to develop a semi-active seismic response control system. Although the finite element method (FEM) is the most widely used method for seismic response analysis, when the FEM is applied to the dynamic analysis of building structures with nonlinear semi-active control devices, the computational effort required for the simulation for optimal design of the semi-active control system can be considerable. To solve this problem, this paper used recurrent neural network (RNN) to make a time history response simulation model for building structures with a semi-active control system. Example structures were selected of an 11-story building structure with a semi-active tuned mass damper (TMD), and a 27-story building having a semi-active mid-story isolation system. A magnetorheological damper was used as the semi-active control device. Five historical earthquakes and five artificial ground motions were used as ground excitations to train the RNN model. Two artificial ground motions and one historical earthquake, which were not used for training, were used to verify the developed the RNN model. Compared to the FEM model, the developed RNN model could effectively provide very accurate seismic responses, with significantly reduced computational cost.

Deep learning and signal processing based algorithm for autorecognition of harmonic loads

2021 ◽  
pp. 1-14
Author(s):  
Pullabhatla Srikanth ◽  
Chiranjib Koley
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Power System ◽  
Learning Algorithm ◽  
Probabilistic Neural Network ◽  
Nonlinear Loads ◽  
Nonlinear Load ◽  
Interconnected Power System ◽  
Time Frequency ◽  
Deep Learning Algorithm

A convolution neural network (CNN) based deep learning method has been proposed for automatic classification and localization of nonlinear loads present in an interconnected power system. The identification of nonlinear loads has been previously dealt with the use of Nonlinear Auto Regression neural network with eXogenous inputs (NARX), Backpropagation Neural Network (BPNN), Probabilistic Neural Network (PNN), Artificial Neural Networks (ANN) and Fuzzy Logic (FL). However, these techniques had not explored the area of classification of industrial and domestic nonlinear loads in an interconnected power system. Also, a Deep learning-based solution for identification of the type of nonlinear load has not been reported in the literature to date. Hence, to address these shortcomings, an IEEE-9 Bus system with industrial nonlinear loads has been used to obtain various current waveforms with distortions. The recorded current waveforms are transformed into a time-frequency (TF) domain plane, and the obtained images are then fed to the deep learning algorithm. The colored images of the TF plots of each type of nonlinear load in Red-Green-Blue (RGB) index provide the best visual features for extraction. The TF domain signatures of individual events are scaled to a standard size before feeding to the algorithm. Through these TF signatures, unique features were extracted with the deep learning algorithm, and then passed on to different stages of convolution and max-pooling with fully connected layers. The softmax classifier at the end classifies the input data into the type of nonlinear present in the power system. The algorithm, when run at different buses, also identifies the location of the nonlinear load. The proposed methodology avoids the usage of any additional fusion layer for obtaining unique features, reduces the training time and maintains the highest accuracy of 100%.

Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

2005 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
Noraliza Hamzah ◽  
Wan Nor Ainin Wan Abdullah ◽  
Pauziah Mohd Arsad
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Microsoft Excel ◽  
Waveform Data ◽  
The Neural Network ◽  
Harmonic Voltage ◽  
Power Quality Disturbances ◽  
Electronic Load ◽  
Wavelet Technique

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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