scholarly journals An Extreme Learning Machine Based on Artificial Immune System

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Hui-yuan Tian ◽  
Shi-jian Li ◽  
Tian-qi Wu ◽  
Min Yao
Keyword(s):  
Immune System ◽  
Extreme Learning Machine ◽  
Artificial Immune System ◽  
Superior Performance ◽  
Artificial Immune ◽  
Generalization Performance ◽  
Good Convergence ◽  
Fast Training ◽  
Benchmark Datasets ◽  
Learning Machine

Extreme learning machine algorithm proposed in recent years has been widely used in many fields due to its fast training speed and good generalization performance. Unlike the traditional neural network, the ELM algorithm greatly improves the training speed by randomly generating the relevant parameters of the input layer and the hidden layer. However, due to the randomly generated parameters, some generated “bad” parameters may be introduced to bring negative effect on the final generalization ability. To overcome such drawback, this paper combines the artificial immune system (AIS) with ELM, namely, AIS-ELM. With the help of AIS’s global search and good convergence, the randomly generated parameters of ELM are optimized effectively and efficiently to achieve a better generalization performance. To evaluate the performance of AIS-ELM, this paper compares it with relevant algorithms on several benchmark datasets. The experimental results reveal that our proposed algorithm can always achieve superior performance.

An Artificial Immune System for Frequency-Variable Air Conditioner

2010 ◽  
Vol 34-35 ◽  
pp. 1449-1452
Author(s):  
Xue Peng Liu ◽  
Dong Mei Zhao ◽  
Bin Wang
Keyword(s):  
Immune Response ◽  
Immune System ◽  
Pid Controller ◽  
Artificial Immune System ◽  
Stable Condition ◽  
Antibody Concentration ◽  
Air Conditioner ◽  
Artificial Immune ◽  
Good Convergence ◽  
Abnormal Behaviors

It is common to control the Frequency-variable air conditioner (A/C) by using PID controller. However, an arithmetic based on artificial immune system was proposed. The immune system of organism was analyzed, and an architecture of the arithmetic was designed. The A/C behaviors were expressed by antibodies, a concentration model of antibody was built, and rules of A/C behaviors could be obtained by the antibody concentration. the initial immune response arithmetic and the secondary immune response arithmetic were designed, which were used to memorized normal behaviors and detect abnormal behaviors. Experiments show that the scheme is capable of adapting to system variation. The system can obtain the stable condition with good convergence even high temperature of 45°C

scholarly journals Extreme Learning Machine Based on Firefly Adaptive Flower Pollination Algorithm Optimization

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1583
Author(s):  
Ting Liu ◽  
Qinwei Fan ◽  
Qian Kang ◽  
Lei Niu
Keyword(s):  
Extreme Learning Machine ◽  
Credit Rating ◽  
Nonlinear Function ◽  
Flower Pollination Algorithm ◽  
Algorithm Optimization ◽  
Classification Problems ◽  
Generalization Performance ◽  
Flower Pollination ◽  
Fast Training ◽  
Learning Machine

Extreme learning machine (ELM) has aroused a lot of concern and discussion for its fast training speed and good generalization performance, and it has been used diffusely in both regression and classification problems. However, on account of the randomness of input parameters, it requires more hidden nodes to obtain the desired accuracy. In this paper, we come up with a firefly-based adaptive flower pollination algorithm (FA-FPA) to optimize the input weights and thresholds of the ELM algorithm. Nonlinear function fitting, iris classification and personal credit rating experiments show that the ELM with FA-FPA (FA-FPA-ELM) can obtain significantly better generalization performance (such as root mean square error, classification accuracy) than traditional ELM, ELM with firefly algorithm (FA-ELM), ELM with flower pollination algorithm (FPA-ELM), ELM with genetic algorithm (GA-ELM) and ELM with particle swarm optimization (PSO-ELM) algorithms.

scholarly journals Fault diagnosis of industrial robot reducer by an extreme learning machine with a level-based learning swarm optimizer

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110195
Author(s):  
Jianwen Guo ◽  
Xiaoyan Li ◽  
Zhenpeng Lao ◽  
Yandong Luo ◽  
Jiapeng Wu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Production Efficiency ◽  
Industrial Robot ◽  
Optimal Solution ◽  
Industrial Robots ◽  
Generalization Performance ◽  
Gradient Descent Algorithm ◽  
Learning Machine

Fault diagnosis is of great significance to improve the production efficiency and accuracy of industrial robots. Compared with the traditional gradient descent algorithm, the extreme learning machine (ELM) has the advantage of fast computing speed, but the input weights and the hidden node biases that are obtained at random affects the accuracy and generalization performance of ELM. However, the level-based learning swarm optimizer algorithm (LLSO) can quickly and effectively find the global optimal solution of large-scale problems, and can be used to solve the optimal combination of large-scale input weights and hidden biases in ELM. This paper proposes an extreme learning machine with a level-based learning swarm optimizer (LLSO-ELM) for fault diagnosis of industrial robot RV reducer. The model is tested by combining the attitude data of reducer gear under different fault modes. Compared with ELM, the experimental results show that this method has good stability and generalization performance.

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