Withdrawal: He, Y., Zhang, J., Bio‐Inspired Algorithms For Industrial Robot Control Using A Deep Learning Method. Agronomy Journal. 2022; Accepted Articles. https://doi.org/10.1002/agj2.21214.

Abstract

AbstractBio‐inspired computing is a research method designed to address problems through computational methods based on biology and the natural environment. Bio‐inspired computing often grows and expands on a tiny foundation of certain laws through unattained deep learning. Importantly there is a dramatic increase in the research about bio‐inspired algorithms and techniques, from machine learning algorithms to other techniques based on values or modelling techniques of biological processes. The challenges of presenting and implementing complex strategies in practice generate new and innovative solutions in typically complicated areas. The areas and problems in which machine learning normally manages enable everyone to explore new challenges in bio‐inspired calculation methodologies. To manage these kinds of complexity, the implementation of bio‐inspired algorithms using the deep soft clustering (BiADSC) approach has been proposed to analyze complicated dynamics demands for human intelligence, which will take time and create a trade‐off between the dimension of the state and controllability. Deep novel framework focus on classification tasks through a combination of domain‐based extracting features with revolutionary neural networks (RNN) method. Furthermore, one of the deep learning models includes high‐performance neural networks using soft clustering algorithms. The clustering algorithm provides the data of basic units by local action that can communicate either direct or indirect way locally. Experimental results show that the proposed architecture enables in‐time data access and can successfully serve a range of simultaneous applications with a performance of 98.83%.This article is protected by copyright. All rights reserved