Artificial Neural Network Trained by Plant Genetic-Inspired Optimizer

  • Neeraj Gupta
  • Mahdi KhosravyEmail author
  • Nilesh Patel
  • Saurabh Gupta
  • Gazal Varshney
Part of the Springer Tracts in Nature-Inspired Computing book series (STNIC)


As a great computational intelligence technique, artificial neural networks (ANNs) have intensively attracted the interest of researchers of artificial intelligence. Due to the easy implementation of ANN, vast types of structures and associated rules, their successful application can be seen in real-life and industrial problems. From a wide variety of ANN such as feed-forward ANN, Kohonen self-organizing ANN, radial basis function (RBF) ANN, and spiking ANN, we describe a multi-layer perceptron ANN with the focus on designing AI-based condition monitoring system. This chapter presents the neuroevolution-based monitoring system to detect the oil filter condition in agricultural (Ag) machines using meta-heuristic METO algorithm. Evolutionary learning algorithm finds the optimal weights of ANN along with the behavior of each neuron.


Artificial neural networks Plants genetic-inspired optimization Meta-heuristic optimization 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Neeraj Gupta
    • 1
  • Mahdi Khosravy
    • 2
    • 3
    Email author
  • Nilesh Patel
    • 1
  • Saurabh Gupta
    • 4
    • 5
  • Gazal Varshney
    • 6
  1. 1.Department of Computer Science and EngineeringOakland UniversityRochesterUSA
  2. 2.Media Integrated Communication Lab, Graduate School of EngineeringOsaka UniversityOsakaJapan
  3. 3.Electrical Engineering DepartmentFederal University of Juiz de ForaJuiz de ForaBrazil
  4. 4.Department of Advanced EngineeringJohn Deere India Pvt. Ltd.PuneIndia
  5. 5.Research Scholar, Department of Computer ScienceBanasthali VidyapithVanasthaliIndia
  6. 6.University of Information Science and TechnologyOhridNorth Macedonia

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