scholarly journals Bifurcation and Hybrid Control for A Simple Hopfield Neural Networks with Delays

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Zisen Mao ◽  
Hao Wang ◽  
Dandan Xu ◽  
Zhoujin Cui
Keyword(s):  
Neural Network ◽  
Hopf Bifurcation ◽  
Control Strategy ◽  
State Feedback ◽  
Hybrid Control ◽  
Hopfield Neural Network ◽  
Parameter Perturbation ◽  
Hybrid Controller ◽  
Simulation Results ◽  
Delayed State Feedback

A detailed analysis on the Hopf bifurcation of a delayed Hopfield neural network is given. Moreover, a new hybrid control strategy is proposed, in which time-delayed state feedback and parameter perturbation are used to control the Hopf bifurcation of the model. Numerical simulation results confirm that the new hybrid controller using time delay is efficient in controlling Hopf bifurcation.

HYBRID CONTROL OF BIFURCATION IN CONTINUOUS NONLINEAR DYNAMICAL SYSTEMS

2005 ◽  
Vol 15 (12) ◽  
pp. 3895-3903 ◽  
Author(s):  
ZENGRONG LIU ◽  
K. W. CHUNG
Keyword(s):  
Dynamical System ◽  
Hopf Bifurcation ◽  
Dynamical Systems ◽  
Control Strategy ◽  
State Feedback ◽  
Hybrid Control ◽  
Nonlinear Dynamical Systems ◽  
Parameter Perturbation ◽  
Nonlinear Dynamical ◽  
Dimensional Dynamical System

In this paper, a new hybrid control strategy is proposed, in which state feedback and parameter perturbation are used to control the bifurcations of continuous dynamical systems. The hybrid control can be applied to any component of a several dimensional dynamical system and is still effective even when the system becomes chaotic. Our results show that various bifurcations, such as Hopf bifurcation and Poincaré bifurcation, can be controlled by means of this method.

scholarly journals Controlling Hopf Bifurcation of a New Modified Hyperchaotic Lü System

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Ping Cai ◽  
Jia-Shi Tang ◽  
Zhen-Bo Li
Keyword(s):  
Hopf Bifurcation ◽  
Control Strategy ◽  
State Feedback ◽  
Hybrid Control ◽  
Normal Form Theory ◽  
Lü System ◽  
Form Theory ◽  
Hyperchaotic Lu System ◽  
The Stability ◽  
Lu System

Controlling Hopf bifurcation of a new modified hyperchaotic Lü system is investigated in this paper. A hybrid control strategy using both state feedback and parameter control is proposed. The control strategy realizes the delay of Hopf bifurcation. Furthermore, by applying the normal form theory, the stability of the bifurcation is determined. Numerical simulation results are given to support the theoretical analysis.

The Design of a Wavelet-Based Neural Network Adaptive Filter

2013 ◽  
Vol 860-863 ◽  
pp. 2791-2795
Author(s):  
Qian Xiao ◽  
Yu Shan Jiang ◽  
Ru Zheng Cui
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Real Time ◽  
Adaptive Filter ◽  
Adaptive Algorithm ◽  
Distributed Storage ◽  
Hopfield Neural Network ◽  
Lms Algorithm ◽  
The Neural Network ◽  
Simulation Results

Aiming at the large calculation workload of adaptive algorithm in adaptive filter based on wavelet transform, affecting the filtering speed, a wavelet-based neural network adaptive filter is constructed in this paper. Since the neural network has the ability of distributed storage and fast self-evolution, use Hopfield neural network to implement adaptive filter LMS algorithm in this filter so as to improve the speed of operation. The simulation results prove that, the new filter can achieve rapid real-time denoising.

Simulation of Control Model Based on Optimal Structure of HOPFIELD Neural Network

2014 ◽  
Vol 556-562 ◽  
pp. 2370-2374
Author(s):  
Jing Liu
Keyword(s):  
Neural Network ◽  
Development Strategy ◽  
Financial System ◽  
Hopfield Neural Network ◽  
Control Model ◽  
Low Carbon ◽  
Low Carbon Economy ◽  
Model Based ◽  
Simulation Results ◽  
Carbon Finance

Carbon finance is the core module of the future development of low carbon economy; it is a new type of competitive force in the construction of long-term sustainable development strategy for global financial institutions. Its development degree directly restricts the healthy development of multiple fields related to carbon finance. In this paper we firstly analyze the market structure characteristics of carbon finance based on international experience, and summary Chinese problems of carbon finance according to the domestic status. On the basis we introduce artificial neural network of P.K.Simpson, and construct the carbon financial control model based on neural network prediction. In order to verify the scientific and practicability of this model, we build dynamic behavior simulation model of carbon financial system. The simulation results show that the model can make accurate predictions on scientific decision on carbon financial system application, and the simulation results are relatively complete.

scholarly journals Hopf Bifurcation Control in a FAST TCP and RED Model via Multiple Control Schemes

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Dawei Ding ◽  
Chun Wang ◽  
Lianghui Ding ◽  
Nian Wang ◽  
Dong Liang
Keyword(s):  
Hopf Bifurcation ◽  
Time Delay ◽  
Feedback Control ◽  
Congestion Control ◽  
State Feedback ◽  
Feedback Controller ◽  
Bifurcation Control ◽  
Fast Tcp ◽  
Hybrid Controller ◽  
Control Schemes

We focus on the Hopf bifurcation control problem of a FAST TCP model with RED gateway. The system gain parameter is chosen as the bifurcation parameter, and the stable region and stability condition of the congestion control model are given by use of the linear stability analysis. When the system gain passes through a critical value, the system loses the stability and Hopf bifurcation occurs. Considering the negative influence caused by Hopf bifurcation, we apply state feedback controller, hybrid controller, and time-delay feedback controller to postpone the onset of undesirable Hopf bifurcation. Numerical simulations show that the hybrid controller is the most sensitive method to delay the Hopf bifurcation with identical parameter conditions. However, nonlinear state feedback control and time-delay feedback control schemes have larger control parameter range in the Internet congestion control system with FAST TCP and RED gateway. Therefore, we can choose proper control method based on practical situation including unknown conditions or parameter requirements. This paper plays an important role in setting guiding system parameters for controlling the FAST TCP and RED model.

Predictive Control of DC/DC Converter Based on State Observer

2012 ◽  
Vol 476-478 ◽  
pp. 542-546 ◽  
Author(s):  
Chen Zeng ◽  
Deng Min Pan ◽  
Li Yan Zhang
Keyword(s):  
Neural Network ◽  
Predictive Control ◽  
Control Strategy ◽  
Network Optimization ◽  
State Observer ◽  
Single Sample ◽  
Neural Network Optimization ◽  
Current State ◽  
Simulation Results ◽  
Control State

In this paper, we present an advanced way to control the DC/DC converter by using predictive control. As the current state of the circuit must be known while using the predictive control, state observer is applied to solve the problem that some variables of DC/DC converter can not be observed. Neural network optimization is used to solve the QP problems in single sample step of predictive control. Simulation results show that this approach can utilize fast converge property of neural network and the new control strategy turns out to be very efficiency.

Hybrid Control of a Pneumatic Gantry Robot for Contour Tracking: Proportional Pressure Versus Proportional Flow Control

2009 ◽  
Author(s):  
Mohammed Abu-Mallouh ◽  
Brian Surgenor ◽  
Sasan Taghizadeh
Keyword(s):  
Flow Control ◽  
Coulomb Friction ◽  
Hybrid Control ◽  
Tangential Velocity ◽  
Pressure Control ◽  
Contour Tracking ◽  
Hybrid Controller ◽  
Simulation Results ◽  
Gantry Robot ◽  
Proportional Flow

The application of a pneumatic gantry robot to contour tracking is examined. A hybrid controller is structured to control the contact force and the tangential velocity, simultaneously. In a previous study, experimental contour tracking results for the robot were obtained with electronic proportional pressure control (PPC) valves. The results demonstrated the potential of pneumatic actuation for contour tracking applications. In another study it was found that improvement in performance was limited by system lag and Coulomb friction. A neural network (NN) compensator was developed to counter both effects. Simulation results demonstrated the effectiveness of the NN compensator. Although improvement in performance with NN compensation was significant, this was offset by the requirement for substantive design effort. This paper shows experimentally that equally significant improvement can be achieved by switching from PPC valves to proportional flow control (PFC) valves. The PFC approach requires less design effort.

scholarly journals Hopf Bifurcation Control in a Delayed Predator-Prey System with Prey Infection and Modified Leslie-Gower Scheme

2013 ◽  
Vol 2013 ◽  
pp. 1-11
Author(s):  
Zizhen Zhang ◽  
Huizhong Yang
Keyword(s):  
Hopf Bifurcation ◽  
State Feedback ◽  
Center Manifold ◽  
Controlled System ◽  
Predator Prey ◽  
Hybrid Controller ◽  
Prey System ◽  
The Stability ◽  
Manifold Theory ◽  
Bifurcation And Stability

Hopf bifurcation of a delayed predator-prey system with prey infection and the modified Leslie-Gower scheme is investigated. The conditions for the stability and existence of Hopf bifurcation of the system are obtained. The state feedback and parameter perturbation are used for controlling Hopf bifurcation in the system. In addition, direction of Hopf bifurcation and stability of the bifurcated periodic solutions of the controlled system are obtained by using normal form and center manifold theory. Finally, numerical simulation results are presented to show that the hybrid controller is efficient in controlling Hopf bifurcation.

scholarly journals Exploring the Potentialities of Deep Reinforcement Learning for Incentive-Based Demand Response in a Cluster of Small Commercial Buildings

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2933
Author(s):  
Davide Deltetto ◽  
Davide Coraci ◽  
Giuseppe Pinto ◽  
Marco Savino Piscitelli ◽  
Alfonso Capozzoli
Keyword(s):  
Reinforcement Learning ◽  
Demand Response ◽  
Control Strategy ◽  
Energy Demand ◽  
Hybrid Control ◽  
Renewable Energy Sources ◽  
Economic Benefits ◽  
Commercial Buildings ◽  
Electricity Sector ◽  
Hybrid Controller

Demand Response (DR) programs represent an effective way to optimally manage building energy demand while increasing Renewable Energy Sources (RES) integration and grid reliability, helping the decarbonization of the electricity sector. To fully exploit such opportunities, buildings are required to become sources of energy flexibility, adapting their energy demand to meet specific grid requirements. However, in most cases, the energy flexibility of a single building is typically too small to be exploited in the flexibility market, highlighting the necessity to perform analysis at a multiple-building scale. This study explores the economic benefits associated with the implementation of a Reinforcement Learning (RL) control strategy for the participation in an incentive-based demand response program of a cluster of commercial buildings. To this purpose, optimized Rule-Based Control (RBC) strategies are compared with a RL controller. Moreover, a hybrid control strategy exploiting both RBC and RL is proposed. Results show that the RL algorithm outperforms the RBC in reducing the total energy cost, but it is less effective in fulfilling DR requirements. The hybrid controller achieves a reduction in energy consumption and energy costs by respectively 7% and 4% compared to a manually optimized RBC, while fulfilling DR constraints during incentive-based events.

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