scholarly journals A Brief Review of Neural Networks Based Learning and Control and Their Applications for Robots

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Yiming Jiang ◽  
Chenguang Yang ◽  
Jing Na ◽  
Guang Li ◽  
Yanan Li ◽  
...  
Keyword(s):  
Neural Networks ◽  
Nonlinear Systems ◽  
Human Robot Interaction ◽  
Learning Tools ◽  
Robot Interaction ◽  
Practical Applications ◽  
Powerful Learning ◽  
Wide Range ◽  
Complex Nonlinear Systems ◽  
And Control

As an imitation of the biological nervous systems, neural networks (NNs), which have been characterized as powerful learning tools, are employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification, and patterns recognition. This article aims to bring a brief review of the state-of-the-art NNs for the complex nonlinear systems by summarizing recent progress of NNs in both theory and practical applications. Specifically, this survey also reviews a number of NN based robot control algorithms, including NN based manipulator control, NN based human-robot interaction, and NN based cognitive control.

Application of neural networks in membrane separation

2020 ◽  
Vol 36 (2) ◽  
pp. 265-310 ◽  
Author(s):  
Morteza Asghari ◽  
Amir Dashti ◽  
Mashallah Rezakazemi ◽  
Ebrahim Jokar ◽  
Hadi Halakoei
Keyword(s):  
Neural Networks ◽  
Chemical Engineering ◽  
Membrane Separation ◽  
Mechanistic Model ◽  
Computing Time ◽  
Permeate Flux ◽  
Practical Applications ◽  
Advantages And Disadvantages ◽  
Wide Range ◽  
Physical And Chemical

AbstractArtificial neural networks (ANNs) as a powerful technique for solving complicated problems in membrane separation processes have been employed in a wide range of chemical engineering applications. ANNs can be used in the modeling of different processes more easily than other modeling methods. Besides that, the computing time in the design of a membrane separation plant is shorter compared to many mass transfer models. The membrane separation field requires an alternative model that can work alone or in parallel with theoretical or numerical types, which can be quicker and, many a time, much more reliable. They are helpful in cases when scientists do not thoroughly know the physical and chemical rules that govern systems. In ANN modeling, there is no requirement for a deep knowledge of the processes and mathematical equations that govern them. Neural networks are commonly used for the estimation of membrane performance characteristics such as the permeate flux and rejection over the entire range of the process variables, such as pressure, solute concentration, temperature, superficial flow velocity, etc. This review investigates the important aspects of ANNs such as methods of development and training, and modeling strategies in correlation with different types of applications [microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), reverse osmosis (RO), electrodialysis (ED), etc.]. It also deals with particular types of ANNs that have been confirmed to be effective in practical applications and points out the advantages and disadvantages of using them. The combination of ANN with accurate model predictions and a mechanistic model with less accurate predictions that render physical and chemical laws can provide a thorough understanding of a process.

An Optimization Procedure of Model’s Base Construction in Multimodel Representation of Complex Nonlinear Systems

2021 ◽  
Author(s):  
Bennasr Hichem ◽  
M’Sahli Faouzi
Keyword(s):  
Nonlinear Systems ◽  
Learning Algorithm ◽  
Batch Reactor ◽  
Optimization Procedure ◽  
Base Number ◽  
Deterministic Approach ◽  
Modeling Analysis ◽  
Complex Nonlinear Systems ◽  
Definition Of ◽  
And Control

The multimodel approach is a research subject developed for modeling, analysis and control of complex systems. This approach supposes the definition of a set of simple models forming a model’s library. The number of models and the contribution of their validities is the main issues to consider in the multimodel approach. In this chapter, a new theoretical technique has been developed for this purpose based on a combination of probabilistic approaches with different objective function. First, the number of model is constructed using neural network and fuzzy logic. Indeed, the number of models is determined using frequency-sensitive competitive learning algorithm (FSCL) and the operating clusters are identified using Fuzzy K- means algorithm. Second, the Models’ base number is reduced. Focusing on the use of both two type of validity calculation for each model and a stochastic SVD technique is used to evaluate their contribution and permits the reduction of the Models’ base number. The combination of FSCL algorithms, K-means and the SVD technique for the proposed concept is considered as a deterministic approach discussed in this chapter has the potential to be applied to complex nonlinear systems with dynamic rapid. The recommended approach is implemented, reviewed and compared to academic benchmark and semi-batch reactor, the results in Models’ base reduction is very important witch gives a good performance in modeling.

GAIT GENERATION AND OPTIMIZATION USING THE ESTIMATION OF DISTRIBUTION ALGORITHM FOR TEENSIZE HUMANOID SOCCER ROBOT RESr-1

2008 ◽  
Vol 05 (03) ◽  
pp. 437-456 ◽  
Author(s):  
LINGYUN HU ◽  
CHANGJIU ZHOU
Keyword(s):  
Hierarchical Control ◽  
Estimation Of Distribution Algorithm ◽  
Human Robot Interaction ◽  
Cooperative Research ◽  
Soccer Robot ◽  
Robot Interaction ◽  
Planning And Control ◽  
Hierarchical Control Architecture ◽  
Estimation Of Distribution ◽  
And Control

This paper gives an overview of locomotion planning and control of a TeenSize humanoid soccer robot, Robo-Erectus Senior (RESr-1), which has been developed as an experimental platform for human–robot interaction and cooperative research in general and robotics soccer games in particular. The locomotion planning and control, along with an introduction of hierarchical control architecture, vision-based behavior and its application in the Humanoid TeenSize soccer challenge, are elaborated. The Estimation of Distribution Algorithm (EDA) is used in locomotion generation and optimization to achieves dynamically stable walk and a powerful kick. By setting different objective functions, smooth walking and powerful kicking can be generated quickly. RESr-1 made its debut at RoboCup 2007, and got fourth place in the Humanoid TeenSize penalty kick competition. In addition, some experimental results on RESr-1's walking, tracking and kicking are presented.

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