Path Generation of Mechanism Based on Multimodal Optimization Using an Evolutionary Approach

2004 ◽  
Author(s):  
Renbin Xiao ◽  
Yong Liu ◽  
Gang Dou
Keyword(s):  
Optimization Algorithm ◽  
Optimization Method ◽  
Synthesis Methods ◽  
Immune Network ◽  
Path Generation ◽  
Multimodal Optimization ◽  
Artificial Immune ◽  
Planar Mechanisms ◽  
Artificial Immune Network ◽  
Performance Of Algorithm

This paper presents an optimization method for path generation of planar mechanisms by using artificial immune network based multimodal optimization algorithm. Firstly, the multimodal character of optimal synthesis methods for mechanism path generation is analyzed. Secondly, based on brief introduction of the artificial immune network theory and the AINET based multimodal optimization algorithm Opt-aiNet, the Opt-aiNet algorithm is improved in stopping criterion and parameter selection, which is ground on detailed analysis of the influence of parameters on the performance of algorithm. Then the improved Opt-aiNet algorithm is introduced to solve multimodal model of mechanism path generation. Finally, based upon the case study, the advantages of the improved Opt-aiNet algorithm in solving mechanism path generation problm are discussed and some concluding remarks are drawn.

Optimal Synthesis of Mechanisms for Path Generation Using Refined Numerical Representation Based Model and AIS Based Searching Method

2004 ◽  
Vol 127 (4) ◽  
pp. 688-691 ◽  
Author(s):  
Yong Liu ◽  
Renbin Xiao
Keyword(s):  
Synthesis Method ◽  
Optimal Synthesis ◽  
Numerical Representation ◽  
Path Generation ◽  
Artificial Immune ◽  
Planar Mechanisms ◽  
Artificial Immune Network ◽  
Synthesis Of Mechanisms ◽  
Initial Sampling ◽  
Searching Method

This paper presents an optimal synthesis method for path generation of planar mechanisms, in which a new path-description method named refined numerical representation is proposed to define the object function of the optimization model for path generation, and then the artificial immune network searching method is introduced to search candidate solutions. As a result, desired mechanisms can be generated independent of the scale, rotation, and translation transformation as well as sampling uniformity of initial sampling points. Experiment results demonstrate the effectiveness of the approach.

Identification of cavitation in centrifugal pump by artificial immune network

2021 ◽  
pp. 095440892110284
Author(s):  
Seyed M Matloobi ◽  
Mohammad Riahi
Keyword(s):  
Immune System ◽  
Production Systems ◽  
Principal Component ◽  
Immune Network ◽  
Centrifugal Pumps ◽  
Artificial Immune ◽  
Human Immune System ◽  
Time Frequency ◽  
Artificial Immune Network ◽  
Damage Condition

Reducing the cost of unscheduled shutdowns and enhancing the reliability of production systems is an important goal for various industries; this could be achieved by condition monitoring and artificial intelligence. Cavitation is a common undesired phenomenon in centrifugal pumps, which causes damage and its detection in the preliminary stage is very important. In this paper, cavitation is identified by use of vibration and current signal and artificial immune network that is modeled on the base of the human immune system. For this purpose, first data collection were done by a laboratory setup in health and five stages damage condition; then various features in time, frequency, and time–frequency were extracted from vibration and current signals in addition to pressure and flow rate; next feature selection and dimensions reduction were done by artificial immune method to use for classification; finally, they were used by artificial immune network and some other methods to identify the system condition and classification. The results of this study showed that this method is more accurate in the detection of cavitation in the initial stage compared to methods such as non-linear supportive vector machine, multi-layer artificial neural network, K-means and fuzzy C-means with the same data. Also, selected features with artificial immune system were better than principal component analysis results.

scholarly journals An Associate Rules Mining Algorithm Based on Artificial Immune Network for SAR Image Segmentation

2015 ◽  
Vol 2015 ◽  
pp. 1-14
Author(s):  
Mengling Zhao ◽  
Hongwei Liu
Keyword(s):  
Association Rules ◽  
Large Scale ◽  
Classification Algorithm ◽  
Data Sets ◽  
Immune Network ◽  
Artificial Immune ◽  
Mining Algorithm ◽  
Sar Image Segmentation ◽  
Artificial Immune Network ◽  
Adaptive Pso

As a computational intelligence method, artificial immune network (AIN) algorithm has been widely applied to pattern recognition and data classification. In the existing artificial immune network algorithms, the calculating affinity for classifying is based on calculating a certain distance, which may lead to some unsatisfactory results in dealing with data with nominal attributes. To overcome the shortcoming, the association rules are introduced into AIN algorithm, and we propose a new classification algorithm an associate rules mining algorithm based on artificial immune network (ARM-AIN). The new method uses the association rules to represent immune cells and mine the best association rules rather than searching optimal clustering centers. The proposed algorithm has been extensively compared with artificial immune network classification (AINC) algorithm, artificial immune network classification algorithm based on self-adaptive PSO (SPSO-AINC), and PSO-AINC over several large-scale data sets, target recognition of remote sensing image, and segmentation of three different SAR images. The result of experiment indicates the superiority of ARM-AIN in classification accuracy and running time.

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