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Machines01:19

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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The highest and lowest values of a function, relative to a reference axis, are known as extreme values. These include absolute maximum and absolute minimum values, which represent the highest and lowest points the function reaches across its entire domain. Within a restricted portion of the function, the highest and lowest values are referred to as local maximum and local minimum values, respectively.Periodic functions, such as sine and cosine, show extreme values at infinitely many points due...
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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An Extreme Learning Machine Based on Artificial Immune System.

Hui-Yuan Tian1, Shi-Jian Li1, Tian-Qi Wu1

  • 1School of Computer Science and Technology, Zhejiang University, Hangzhou, China.

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This study introduces the Artificial Immune System-Extreme Learning Machine (AIS-ELM) algorithm. AIS-ELM optimizes random parameters in ELM, enhancing generalization performance and achieving superior results compared to other methods.

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Area of Science:

  • Machine Learning
  • Computational Intelligence
  • Artificial Intelligence

Background:

  • Extreme Learning Machine (ELM) offers fast training and good generalization.
  • Random parameter generation in ELM can lead to suboptimal performance.
  • Improving ELM's generalization ability is crucial for its practical applications.

Purpose of the Study:

  • To enhance the generalization performance of the Extreme Learning Machine algorithm.
  • To address the issue of suboptimal parameters introduced by random initialization in ELM.
  • To propose a novel hybrid algorithm combining Artificial Immune System (AIS) with ELM.

Main Methods:

  • Integration of the Artificial Immune System (AIS) with the Extreme Learning Machine (ELM) algorithm, creating the AIS-ELM model.
  • Utilizing the global search and convergence properties of AIS to optimize ELM's randomly generated parameters.
  • Comparative evaluation of AIS-ELM against other relevant algorithms on benchmark datasets.

Main Results:

  • The proposed AIS-ELM algorithm demonstrates effective and efficient optimization of ELM parameters.
  • AIS-ELM consistently achieves superior performance compared to existing algorithms.
  • Experimental results validate the enhanced generalization ability of the AIS-ELM approach.

Conclusions:

  • The AIS-ELM hybrid algorithm successfully overcomes the limitations of random parameter initialization in standard ELM.
  • AIS-ELM offers improved generalization performance, making it a promising method for various machine learning tasks.
  • The integration of AIS principles provides a robust framework for optimizing ELM and achieving better predictive accuracy.