PERIODIC RESPONSE TO EXTERNAL STIMULATION OF A CHAOTIC NEURAL NETWORK WITH DELAYED FEEDBACK

1999 ◽  
Vol 09 (04) ◽  
pp. 713-722 ◽  
Author(s):  
ALBAN PUI MAN TSUI ◽  
ANTONIA J. JONES
Keyword(s):  
Neural Network ◽  
Chaos Control ◽  
Control Method ◽  
Original System ◽  
Neural System ◽  
Delayed Feedback ◽  
Input Stimulus ◽  
Biologically Inspired ◽  
External Stimulation ◽  
Different Types

We construct a feedforward neural network so that when the outputs are fed back into the inputs and the system is iterated it behaves chaotically. We call this the "rest state". Suppose now that an input stimulus is added to one or more inputs. Following a biologically inspired model suggested by Freeman [1991], under these conditions we should want the behavior of the network to stabilize into an unstable periodic orbit of the original system. We call this the "retrieval behavior" since it is analogous to the act of recognition. Standard methods of chaos control, such as OGY for example, used to elicit the retrieval behavior would be inappropriate, since such methods involve calculations external to the system being controlled and can be considered unlikely in a biological neural network. Using a chaos control method originally suggested by Pyragas [1992] we show that retrieval behavior can occur as a result of delayed feedback and examine the variety of the responses that arise under different types of stimuli and under noise. This artificial neural system has a strong dynamical parallel to Freeman's observed biological phenomenon.

scholarly journals JUSTIFICATION OF PARAMETERS OF THE CONTROL SYSTEM OF ELECTROMECHANICAL DEBALANCE VIBRODRIVE OF VIBRATION MACHINES ON THE BASIS OF ARTIFICIAL NEURAL NETWORK

2021 ◽  
pp. 52-63
Author(s):  
Leonid Yaroshenko ◽  
Roman Chubyk ◽  
Iryna Derevenko
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Mechanical System ◽  
Predictive Model ◽  
Control Method ◽  
Resonant Mode ◽  
Neural System ◽  
Artificial Neural ◽  
Vibration Parameters

The article analyzes and proposes an approach to the construction of a control system for electromechanical debalance vibrodrive for vibration machines based on an artificial neural network. As a result of the analysis of various methods of managing dynamic objects it is concluded that the most appropriate and perfect for this type of machine is neurocontrol method of predictive model neurocontrol, which allows to expand the functionality of these vibrating machines and significantly save energy for vibratory drive of their oscillations. A direct neuro-emulator is used to predict the future behavior of the oscillating mechanical system of the vibration technological machines and to calculate errors. An important feature of the predictive neurocontrol model in the proposed method of controlling the operation of vibrating technological machines using an artificial neural system is that there is no neurocontroller that needs to be trained, its place is taken by the optimization algorithm. Applying the proposed method of controlling operation of adaptive vibration technology machines using artificial neural network will optimize the electromechanical control of debalanced vibration drive of vibrating machines and provide optimal resonant modes of its operation (which is energy efficient) in all technological modes of vibrating operation. The technical and economic characteristics of this control method are further improved due to the fact that the proposed control method uses the technology of predictive model neurocontrol and as a result is constantly calculated (forecasted) several cycles in advance and determines the best strategy to control the frequency of forced cyclic vibration. As a result, the mechanical system of vibration machines spends less time in non-resonant mode. This method of control also minimizes the duration of transients when changing the load mass of the working body vibration or changing the mode of vibration parameters and the parameters of their technological process.

Control of Continuous Time Chaotic Systems With Unknown Dynamics and Limitation on State Measurement

2018 ◽  
Vol 14 (1) ◽  
Author(s):  
Hojjat Kaveh ◽  
Hassan Salarieh
Keyword(s):  
Chaotic Attractor ◽  
Chaos Control ◽  
Chaotic Systems ◽  
Control Method ◽  
Original System ◽  
Control Of Chaos ◽  
State Measurement ◽  
New Chaotic Attractor ◽  
Systematic Control ◽  
Takens Embedding

This paper has dedicated to study the control of chaos when the system dynamics is unknown and there are some limitations on measuring states. There are many chaotic systems with these features occurring in many biological, economical and mechanical systems. The usual chaos control methods do not have the ability to present a systematic control method for these kinds of systems. To fulfill these strict conditions, we have employed Takens embedding theorem which guarantees the preservation of topological characteristics of the chaotic attractor under an embedding named “Takens transformation.” Takens transformation just needs time series of one of the measurable states. This transformation reconstructs a new chaotic attractor which is topologically similar to the unknown original attractor. After reconstructing a new attractor its governing dynamics has been identified. The measurable state of the original system which is one of the states of the reconstructed system has been controlled by delayed feedback method. Then the controlled measurable state induced a stable response to all of the states of the original system.

Controlling the Chaotic n-Scroll Chua's Circuit

2003 ◽  
Vol 13 (09) ◽  
pp. 2709-2714 ◽  
Author(s):  
Yan-Li Zou ◽  
Xiao-Shu Luo ◽  
Pin-Qun Jiang ◽  
Bing-Hong Wang ◽  
Guanrong Chen ◽  
...  
Keyword(s):  
Theoretical Analysis ◽  
Fixed Points ◽  
Chaos Control ◽  
Control Method ◽  
Chua’S Circuit ◽  
System Matrix ◽  
Chua's Circuit ◽  
State Variable ◽  
Different Types ◽  
Linearized System

In this paper, controlling chaos in the chaotic n-scroll Chua's circuit is studied. The approach taken is to use feedback of a single state variable in a simple PD (proportional and differential) format. First, the unstable fixed points in the n-scroll Chua's circuit are classified into two different types according to the characteristics of the eigenvalues of the linearized system matrix at the fixed points. Then, the controllability of these two-types of fixed points is studied. Theoretical analysis shows that the first type of unstable fixed points can be stabilized, at which Hopf bifurcation can also be generated. Control results of the n-scroll Chua's circuit are then demonstrated. Both theoretical analysis and numerical simulation results show that the proposed chaos control method is indeed effective.

Dynamic Decoupling Control of Electro-Spindle Supported by AC Hybrid Magnetic Bearings Based on Neural Network Inverse Method

2010 ◽  
Vol 97-101 ◽  
pp. 2716-2719 ◽  
Author(s):  
Wei Yu Zhang ◽  
Huang Qiu Zhu ◽  
Ze Bin Yang
Keyword(s):  
Neural Network ◽  
System Theory ◽  
Control Method ◽  
Coupled System ◽  
Original System ◽  
Rotor System ◽  
Inverse System ◽  
Decoupling Control ◽  
Magnetic Bearings ◽  
Dynamic Decoupling

A dynamic decoupling control method based on neural network inverse system theory is developed for the 5 degrees of freedom (5-DOF) rotor system. The rotor system suspended by AC hybrid magnetic bearings (HMBs) is a multivariable, nonlinear and strong coupled system. Firstly, the configuration of 5-DOF HMBs and the mathematical equations of suspension forces are set up. Secondly, it is demonstrated the system is reversible by analyzing mathematical model. On the basis, the neural network inverse system which is composed of the static neural networks and integrators, and original system are in series to constitute pseudo linear systems. Finally, linear system theory is applied to these linearization subsystems for designing close-loop controllers. The simulation results show that this kind of control strategy can realize dynamic decoupling control, and control system obtains good dynamic and static performances.

Squeak and rattle noise classification using radial basis function neural networks

2020 ◽  
Vol 68 (4) ◽  
pp. 283-293
Author(s):  
Oleksandr Pogorilyi ◽  
Mohammad Fard ◽  
John Davy ◽  
Mechanical and Automotive Engineering, School ◽  
Mechanical and Automotive Engineering, School ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
High Accuracy ◽  
Training Method ◽  
Vehicle Interior ◽  
Trained Classifier ◽  
Different Types ◽  
Noise Classification ◽  
Automatic Tool ◽  
Multi Class Classification

In this article, an artificial neural network is proposed to classify short audio sequences of squeak and rattle (S&R) noises. The aim of the classification is to see how accurately the trained classifier can recognize different types of S&R sounds. Having a high accuracy model that can recognize audible S&R noises could help to build an automatic tool able to identify unpleasant vehicle interior sounds in a matter of seconds from a short audio recording of the sounds. In this article, the training method of the classifier is proposed, and the results show that the trained model can identify various classes of S&R noises: simple (binary clas- sification) and complex ones (multi class classification).

scholarly journals Framework for Indoor Elements Classification via Inductive Learning on Floor Plan Graphs

2021 ◽  
Vol 10 (2) ◽  
pp. 97
Author(s):  
Jaeyoung Song ◽  
Kiyun Yu
Keyword(s):  
Neural Network ◽  
Inductive Learning ◽  
Network Data ◽  
Floor Plan ◽  
Vector Data ◽  
Adjacency Graph ◽  
Different Types ◽  
Image Pixels ◽  
Learning Frameworks ◽  
New Framework

This paper presents a new framework to classify floor plan elements and represent them in a vector format. Unlike existing approaches using image-based learning frameworks as the first step to segment the image pixels, we first convert the input floor plan image into vector data and utilize a graph neural network. Our framework consists of three steps. (1) image pre-processing and vectorization of the floor plan image; (2) region adjacency graph conversion; and (3) the graph neural network on converted floor plan graphs. Our approach is able to capture different types of indoor elements including basic elements, such as walls, doors, and symbols, as well as spatial elements, such as rooms and corridors. In addition, the proposed method can also detect element shapes. Experimental results show that our framework can classify indoor elements with an F1 score of 95%, with scale and rotation invariance. Furthermore, we propose a new graph neural network model that takes the distance between nodes into account, which is a valuable feature of spatial network data.

scholarly journals An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor

2021 ◽  
Vol 11 (6) ◽  
pp. 2685
Author(s):  
Guojin Pei ◽  
Ming Yu ◽  
Yaohui Xu ◽  
Cui Ma ◽  
Houhu Lai ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Pid Controller ◽  
Experimental Validation ◽  
Control Method ◽  
Smith Predictor ◽  
Constant Force ◽  
Matlab Simulation ◽  
Adjustment Time

A compliant constant-force actuator based on the cylinder is an important tool for the contact operation of robots. Due to the nonlinearity and time delay of the pneumatic system, the traditional proportional–integral–derivative (PID) method for constant force control does not work so well. In this paper, an improved PID control method combining a backpropagation (BP) neural network and the Smith predictor is proposed. Through MATLAB simulation and experimental validation, the results show that the proposed method can shorten the maximum overshoot and the adjustment time compared with traditional the PID method.

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