Dynamics of the Fractional-Order Lorenz System Based on Adomian Decomposition Method and Its DSP Implementation

Dear Editor,Dynamics and digital circuit implementation of the fractional-order Lorenz system are investigated by employing Adomian decomposition method(ADM).Dynamics of the fractional-order Lorenz system with derivative order and parameter varying is analyzed by means of Lyapunov exponents(LEs),bifurcation diagram.

Dynamics and synchronization in a memristor-coupled discrete heterogeneous neuron network considering noise

Research on discrete memristor-based neural networks has received much attention.However,current research mainly focuses on memristor–based discrete homogeneous neuron networks,while memristor-coupled discrete heterogeneous neuron networks are rarely reported.In this study,a new four-stable discrete locally active memristor is proposed and its nonvolatile and locally active properties are verified by its power-off plot and DC V–I diagram.Based on two-dimensional(2D)discrete Izhikevich neuron and 2D discrete Chialvo neuron,a heterogeneous discrete neuron network is constructed by using the proposed discrete memristor as a coupling synapse connecting the two heterogeneous neurons.Considering the coupling strength as the control parameter,chaotic firing,periodic firing,and hyperchaotic firing patterns are revealed.In particular,multiple coexisting firing patterns are observed,which are induced by different initial values of the memristor.Phase synchronization between the two heterogeneous neurons is discussed and it is found that they can achieve perfect synchronous at large coupling strength.Furthermore,the effect of Gaussian white noise on synchronization behaviors is also explored.We demonstrate that the presence of noise not only leads to the transition of firing patterns,but also achieves the phase synchronization between two heterogeneous neurons under low coupling strength.

Dynamical behavior of memristor-coupled heterogeneous discrete neural networks with synaptic crosstalk

Synaptic crosstalk is a prevalent phenomenon among neuronal synapses,playing a crucial role in the transmission of neural signals.Therefore,considering synaptic crosstalk behavior and investigating the dynamical behavior of discrete neural networks are highly necessary.In this paper,we propose a heterogeneous discrete neural network(HDNN)consisting of a three-dimensional KTz discrete neuron and a Chialvo discrete neuron.These two neurons are coupled mutually by two discrete memristors and the synaptic crosstalk is considered.The impact of crosstalk strength on the firing behavior of the HDNN is explored through bifurcation diagrams and Lyapunov exponents.It is observed that the HDNN exhibits different coexisting attractors under varying crosstalk strengths.Furthermore,the influence of different crosstalk strengths on the synchronized firing of the HDNN is investigated,revealing a gradual attainment of phase synchronization between the two discrete neurons as the crosstalk strength decreases.

A novel variable-order fractional chaotic map and its dynamics

In recent years,fractional-order chaotic maps have been paid more attention in publications because of the memory effect.This paper presents a novel variable-order fractional sine map(VFSM)based on the discrete fractional calculus.Specially,the order is defined as an iterative function that incorporates the current state of the system.By analyzing phase diagrams,time sequences,bifurcations,Lyapunov exponents and fuzzy entropy complexity,the dynamics of the proposed map are investigated comparing with the constant-order fractional sine map.The results reveal that the variable order has a good effect on improving the chaotic performance,and it enlarges the range of available parameter values as well as reduces non-chaotic windows.Multiple coexisting attractors also enrich the dynamics of VFSM and prove its sensitivity to initial values.Moreover,the sequence generated by the proposed map passes the statistical test for pseudorandom number and shows strong robustness to parameter estimation,which proves the potential applications in the field of information security.

Method of improving pedestrian navigation performance based on chest card

With the development of positioning technology,loca-tion services are constantly in demand by people.As a primary location service pedestrian navigation has two main approaches based on radio and inertial navigation.The pedestrian naviga-tion based on radio is subject to environmental occlusion lead-ing to the degradation of positioning accuracy.The pedestrian navigation based on micro-electro-mechanical system inertial measurement unit(MIMU)is less susceptible to environmental interference,but its errors dissipate over time.In this paper,a chest card pedestrian navigation improvement method based on complementary correction is proposed in order to suppress the error divergence of inertial navigation methods.To suppress atti-tude errors,optimal feedback coefficients are established by pedestrian motion characteristics.To extend navigation time and improve positioning accuracy,the step length in subsequent movements is compensated by the first step length.The experi-mental results show that the positioning accuracy of the pro-posed method is improved by more than 47%and 44%com-pared with the pure inertia-based method combined with step compensation and the traditional complementary filtering com-bined method with step compensation.The proposed method can effectively suppress the error dispersion and improve the positioning accuracy.

Generating multi-layer nested chaotic attractor and its FPGA implementation

Complex chaotic sequences are widely employed in real world, so obtaining more complex sequences have received highly interest. For enhancing the complexity of chaotic sequences, a common approach is increasing the scroll-number of attractors. In this paper, a novel method to control system for generating multi-layer nested chaotic attractors is proposed.At first, a piecewise(PW) function, namely quadratic staircase function, is designed. Unlike pulse signals, each level-logic of this function is square constant, and it is easy to realize. Then, by introducing the PW functions to a modified Chua's system with cubic nonlinear terms, the system can generate multi-layer nested Chua's attractors. The dynamical properties of the system are numerically investigated. Finally, the hardware implementation of the chaotic system is used FPGA chip.Experimental results show that theoretical analysis and numerical simulation are right. This chaotic oscillator consuming low power and utilization less resources is suitable for real applications.

Hopf bifurcation and phase synchronization in memristor-coupled Hindmarsh–Rose and FitzHugh–Nagumo neurons with two time delays

A memristor-coupled heterogenous neural network consisting of two-dimensional(2D)FitzHugh–Nagumo(FHN)and Hindmarsh–Rose(HR)neurons with two time delays is established.Taking the time delays as the control parameters,the existence of Hopf bifurcation near the stable equilibrium point in four cases is derived theoretically,and the validity of the Hopf bifurcation condition is verified by numerical analysis.The results show that the two time delays can make the stable equilibrium point unstable,thus leading to periodic oscillations induced by Hopf bifurcation.Furthermore,the time delays in FHN and HR neurons have different effects on the firing activity of neural network.Complex firing patterns,such as quiescent state,chaotic spiking,and periodic spiking can be induced by the time delay in FHN neuron,while the neural network only exhibits quiescent state and periodic spiking with the change of the time delay in HR neuron.Especially,phase synchronization between the heterogeneous neurons is explored,and the results show that the time delay in HR neurons has a greater effect on blocking the synchronization than the time delay in FHN neuron.Finally,the theoretical analysis is verified by circuit simulations.

Optimization Model of Oil-Volume Marking with Tilted Oil Tank

In this paper, the relationship model between the oil volume and the vertically tilting parameter (α), the horizontally tilting parameter (β) and the displayed height of oil (h*) is first constructed with the tilted oil tank. Then, based on the data of the oil output volume at different time of day, an optimization model of oil-volume marking with tilted oil tank is established. Finally, parameters α = 2.2° and β = 3.05° are estimated by using nonlinear least squares method and the marking number of the tank-volume meter is given.

3D Point Cloud Semantic Segmentation Based PAConv and SE_variant

With the increasing popularity of 3D sensors(e.g.,Kinect)and light field cameras,technologies such as driverless,smart home and virtual reality have become hot spots for engineering applications.As an important part of 3D vision tasks,point cloud semantic segmentation has received a lot of attention from researchers.In this work,we focus on realistically collected indoor point cloud data and propose a point cloud semantic segmentation method based on PAConv and SE_variant.The SE_variant module captures global perception from a broad perspective of feature space by fusing different pooling methods,which fully utilize the channel information of point clouds.The effectiveness of the method is verified by comparing with other methods on S3DIS and ScanNetV2 semantic tagging benchmarks,and achieving 65.3%mIoU in S3DIS,47.6%mIoU in ScanNetV2.The results of the ablation experiments verify the effectiveness of the key modules and analyze how to use the attention mechanism to improve the 3D semantic segmentation performance.

Multiple mixed state variable incremental integration for reconstructing extreme multistability in a novel memristive hyperchaotic jerk system with multiple cubic nonlinearity

Memristor-based chaotic systems with infinite equilibria are interesting because they generate extreme multistability.Their initial state-dependent dynamics can be explained in a reduced-dimension model by converting the incremental integration of the state variables into system parameters.However,this approach cannot solve memristive systems in the presence of nonlinear terms other than the memristor term.In addition,the converted state variables may suffer from a degree of divergence.To allow simpler mechanistic analysis and physical implementation of extreme multistability phenomena,this paper uses a multiple mixed state variable incremental integration(MMSVII)method,which successfully reconstructs a four-dimensional hyperchaotic jerk system with multiple cubic nonlinearities except for the memristor term in a three-dimensional model using a clever linear state variable mapping that eliminates the divergence of the state variables.Finally,the simulation circuit of the reduced-dimension system is constructed using Multisim simulation software and the simulation results are consistent with the MATLAB numerical simulation results.The results show that the method of MMSVII proposed in this paper is useful for analyzing extreme multistable systems with multiple higher-order nonlinear terms.

Dynamical behaviors in discrete memristor-coupled small-world neuronal networks

The brain is a complex network system in which a large number of neurons are widely connected to each other and transmit signals to each other.The memory characteristic of memristors makes them suitable for simulating neuronal synapses with plasticity.In this paper,a memristor is used to simulate a synapse,a discrete small-world neuronal network is constructed based on Rulkov neurons and its dynamical behavior is explored.We explore the influence of system parameters on the dynamical behaviors of the discrete small-world network,and the system shows a variety of firing patterns such as spiking firing and triangular burst firing when the neuronal parameterαis changed.The results of a numerical simulation based on Matlab show that the network topology can affect the synchronous firing behavior of the neuronal network,and the higher the reconnection probability and number of the nearest neurons,the more significant the synchronization state of the neurons.In addition,by increasing the coupling strength of memristor synapses,synchronization performance is promoted.The results of this paper can boost research into complex neuronal networks coupled with memristor synapses and further promote the development of neuroscience.

Delay-independent stabilization of nonlinear systems with multiple time-delays and its application in chaos synchronization of Rössler system

Purpose-The purpose of this paper is with delay-independent stabilization of nonlinear systems with multiple time-delays and its application in chaos synchronization of Rössler system.Design/methodology/approach-Based on linear matrix inequality and algebra Riccati matrix equation,the stabilization result is derived to guarantee asymptotically stable and applicated in chaos synchronization of Rössler chaotic system with multiple time-delays.Findings-A controller is designed and added to the nonlinear system with multiple time-delays.The stability of the nonlinear system at its zero equilibrium point is guaranteed by applying the appropriate controller signal based on linear matrix inequality and algebra Riccati matrix equation scheme.Another effective controller is also designed for the global asymptotic synchronization on the Rössler system based on the structure of delay-independent stabilization of nonlinear systems with multiple time-delays.Numerical simulations are demonstrated to verify the effectiveness of the proposed controller scheme.Originality/value-The introduced approach is interesting for delay-independent stabilization of nonlinear systems with multiple time-delays and its application in chaos synchronization of Rössler system.

Firing activities in a fractional-order Hindmarsh–Rose neuron with multistable memristor as autapse

Considering the fact that memristors have the characteristics similar to biological synapses, a fractional-order multistable memristor is proposed in this paper. It is verified that the fractional-order memristor has multiple local active regions and multiple stable hysteresis loops, and the influence of fractional-order on its nonvolatility is also revealed. Then by considering the fractional-order memristor as an autapse of Hindmarsh–Rose(HR) neuron model, a fractional-order memristive neuron model is developed. The effects of the initial value, external excitation current, coupling strength and fractional-order on the firing behavior are discussed by time series, phase diagram, Lyapunov exponent and inter spike interval(ISI) bifurcation diagram. Three coexisting firing patterns, including irregular asymptotically periodic(A-periodic)bursting, A-periodic bursting and chaotic bursting, dependent on the memristor initial values, are observed. It is also revealed that the fractional-order can not only induce the transition of firing patterns, but also change the firing frequency of the neuron. Finally, a neuron circuit with variable fractional-order is designed to verify the numerical simulations.

Synchronization coexistence in a Rulkov neural network based on locally active discrete memristor

At present, many neuron models have been proposed, which can be divided into discrete neuron models and continuous neuron models. Discrete neuron models have the advantage of faster simulation speed and the ease of understanding complex dynamic phenomena. Due to the properties of memorability, nonvolatility, and local activity, locally active discrete memristors(LADMs) are also suitable for simulating synapses. In this paper, we use an LADM to mimic synapses and establish a Rulkov neural network model. It is found that the change of coupling strength and the initial state of the LADM leads to multiple firing patterns of the neural network. In addition, considering the influence of neural network parameters and the initial state of the LADM, numerical analysis methods such as phase diagram and timing diagram are used to study the phase synchronization. As the system parameters and the initial states of the LADM change, the LADM coupled Rulkov neural network exhibits synchronization transition and synchronization coexistence.

Study on Fault Location in the T-Connection Transmission Lines Based on Wavelet Transform

The results of T-Line Traveling Wave Fault Location is easily influenced by the wave arrival time and traveling wave propagation velocity;it proposes that the traveling wave uses wavelet transform to extract the modulus maxima of breakdown voltage, to confirm the time of the traveling wave reaching the three-terminal line. The speed of the traveling wave reaching three terminals is confirmed by the structural parameters of the transmission line. We apply the arrival time and propagation velocity to the T-type traveling wave fault location algorithm. Different transmission line distance select the corresponding algorithm, excluding the impact of fault branches and in some cases ranging accuracy, the failure dead zone will not appear. After MATLAB simulation analysis, the algorithm analysis is clear;the range accuracy is high, so that it can meet the requirements of fault location.

Optimal Design of Transmission Characteristics for Hexagonal Coil Wireless Power Transfer System Based on Genetic Algorithm

Effective optimization methods are used to guide the optimal design of coil parameters,which is significant for improving the transmission performance of the wireless power transfer(WPT)system.Traditional methods mostly rely on the exhaustive attack method and finite element analysis(FEA)to achieve the coil parameter design,which have the disadvantages of complex modeling and time-consumption.To overcome these limitations,this study proposes an optimization strategy based on the genetic algorithm(GA),which considers the actual requirements of the efficiency and power of the WPT system.First,a direct integration method is proposed to simplify the analytical solution process of the mutual inductance between the hexagonal coils.Based on the mutual inductance model,the transmission characteristics of the hexagonal coil WPT system are deeply analyzed by the control variable method.Most importantly,with the proposed optimization objective function and its constraints,the GA is used to automatically achieve multi-parameter optimization of the hexagonal coil.Finally,a 500 W WPT system experimental platform is established,and the experimental results verify the feasibility of the proposed optimization method.

A design method for booster motor of brake-by-wire system based on intelligent electric vehicle

The brake-by-wire(BBW)system is an essential part of the intelligent electric vehicle,which is determination of the braking safety and recovery efficiency.To design a safe and efficient booster motor,the design of booster motor for BBW system is discussed in this paper.Through comparative analysis,experimental simulation and assessment argument,the scheme of designing a booster motor for brake-by-wire system is completely described.First,the mainstream structure of the BBW system and the main challenges it faces in the assisted motor are discussed.Second,comparing the motors of different types and structures,the motor body and control system scheme suitable for the characteristics of the booster motor system are determined.Then,through the simulation analysis of the ansoft and matlab,the optimization scheme of the motor and performance improvement are proposed.Further,through the actual design of a set of the booster motor system,the safe and efficient motor designing are verified,and the problems involving functional safety are discussed.Finally,focus on the problem while simulation and experiment,some important countermeasures to improve current technology and prospect of in-depth study are pointed out.

Legendre Neural Network for Solving Linear Variable Coefficients Delay Differential-Algebraic Equations with Weak Discontinuities

In this paper,we propose a novel Legendre neural network combined with the extreme learning machine algorithm to solve variable coefficients linear delay differential-algebraic equations with weak discontinuities.First,the solution interval is divided into multiple subintervals by weak discontinuity points.Then,Legendre neural network is used to eliminate the hidden layer by expanding the input pattern using Legendre polynomials on each subinterval.Finally,the parameters of the neural network are obtained by training with the extreme learning machine.The numerical examples show that the proposed method can effectively deal with the difficulty of numerical simulation caused by the discontinuities.

Multi-Agent System for Electric Vehicle Charging Scheduling in Parking Lots

As the number of electric vehicles(EVs)increases,massive numbers of EVs have started to gather in commercial parking lots to charge and discharge,which may significantly impact the operation of the grid.There may also be a deviation in the departure time of charged and discharged EVs in commercial parking lots.This deviation can lead to insufficient battery energy when the EVs leave the parking lot.This study uses the simulation software AnyLogic to build a commercial parking lot multi-agent simulation model,and the agent-based model can fully reflect the autonomy of individual EVs.Based on this simulation model,we propose an EV scheduling algorithm.The algorithm contains two main agents.The first is the power distribution center agent(PDCA),which is used to coordinate the energy output of photovoltaic(PV),energy storage system(ESS),and distribution station(DS)to solve the problem of grid overload.The second is the scheduling center agent(SCA),which is used to solve the insufficient battery energy problem due to EVs’random departures.The SCA includes two stages.In the first stage,a priority scheduling algorithm is proposed to emphasize the fairness of EV charging.In the second stage,a genetic algorithm is used to accurately determine the time interval between charging and discharging to ensure the maximum benefit of EV owner.Finally,simulation experiments are conducted in AnyLogic,and the results demonstrate the superiority of the algorithm over the classical algorithm.