Computational Intelligence and Neuroscience

Nature-Inspired Optimization Algorithms for Neuro-Fuzzy Models in Real World Control and Robotics Applications


Publishing date
01 Apr 2019
Status
Published
Submission deadline
30 Nov 2018

Lead Editor

1Tijuana Institute of Technology, Tijuana, Mexico

2Ambedkar Institute of Advanced Communication Technologies and Research, Delhi, India

3Haldia Institute of Technology, Haldia, India


Nature-Inspired Optimization Algorithms for Neuro-Fuzzy Models in Real World Control and Robotics Applications

Description

Nature-inspired optimization algorithms are a recent topic of research and they are based on using of some nature-inspired behavior to solve optimization problems. Currently, a large number of approaches have been developed in this area, such as particle swarm optimization, bat algorithm, ant colony optimization, bee colony, dolphin algorithm, wolf search, flower pollination algorithm, and cat swarm.

However, how to design efficient nature-inspired algorithms and how to use these algorithms for real world application problems in control and robotics are still important issues. In particular, the design of Neuro-Fuzzy Models, like type 2 fuzzy neural networks, type 1 fuzzy neural models, and intuitionistic fuzzy neural networks, has some current interest. In addition, new emerging neural models have been recently proposed. In all these models a common problem is how to obtain an optimal structure, which can be handled by nature-inspired optimization algorithms.

This special issue aims to bring researchers to report their latest research work on development of new nature-inspired algorithms, or innovative applications of existing algorithms in the design of neural models for real world applications in control and robotics, with ultimate goal of exploring future research directions.

Potential topics include but are not limited to the following:

  • Theoretical methods for understanding the behavior of nature-inspired methods
  • Statistical methods for evaluating and parameterizing nature-inspired methods
  • Novel nature-inspired or application-inspired optimization algorithms
  • Statistical approaches for understanding the behavior of nature-inspired methods
  • Optimization of Neuro-Fuzzy Models
  • Optimization of type 2 fuzzy neural network models
  • Optimization of emergent neural models with nature-inspired algorithms
  • Fuzzy logic and intelligent and automatic control
  • Flower pollination algorithm and cat swarm optimization
  • An improved particle swarm optimization algorithm to optimize modular neural networks
  • A fuzzy control design for an autonomous mobile robot using ant colony optimization
  • Comparative study of type 2 fuzzy particle swarm, bee colony, and bat algorithms in optimization of fuzzy controllers
  • Towards the self-adaptation of the bat algorithm
  • Dolphin swarm algorithm
  • Other nature algorithms and their applications

Articles

  • Special Issue
  • - Volume 2019
  • - Article ID 9128451
  • - Editorial

Nature-Inspired Optimization Algorithms for Neuro-Fuzzy Models in Real-World Control and Robotics Applications

Fevrier Valdez | Oscar Castillo | ... | Dipak K. Jana
  • Special Issue
  • - Volume 2019
  • - Article ID 4182639
  • - Research Article

Evolutionary Spiking Neural Networks for Solving Supervised Classification Problems

G. López-Vázquez | M. Ornelas-Rodriguez | ... | H. Rostro-Gonzalez
  • Special Issue
  • - Volume 2019
  • - Article ID 8256723
  • - Research Article

Motion Simulation of Ionic Liquid Gel Soft Actuators Based on CPG Control

Chenghong Zhang | Bin He | ... | Yanmin Zhou
  • Special Issue
  • - Volume 2019
  • - Article ID 4787856
  • - Research Article

Solving the Manufacturing Cell Design Problem through Binary Cat Swarm Optimization with Dynamic Mixture Ratios

Ricardo Soto | Broderick Crawford | ... | Rodrigo Olivares
  • Special Issue
  • - Volume 2019
  • - Article ID 1934575
  • - Research Article

Motion Planning of Autonomous Mobile Robot Using Recurrent Fuzzy Neural Network Trained by Extended Kalman Filter

Qidan Zhu | Yu Han | ... | Chengtao Cai
  • Special Issue
  • - Volume 2018
  • - Article ID 1983897
  • - Research Article

Fuzzy Evaluation of Pharmacokinetic Models

Carlos Sepúlveda | Oscar Montiel | ... | Roberto Sepúlveda
Computational Intelligence and Neuroscience
 Journal metrics
Acceptance rate28%
Submission to final decision79 days
Acceptance to publication38 days
CiteScore2.270
Impact Factor2.154
 Submit

We are committed to sharing findings related to COVID-19 as quickly and safely as possible. Any author submitting a COVID-19 paper should notify us at help@hindawi.com to ensure their research is fast-tracked and made available on a preprint server as soon as possible. We will be providing unlimited waivers of publication charges for accepted articles related to COVID-19. Sign up here as a reviewer to help fast-track new submissions.