A function-based behavioral modeling method for air combat simulation

Today’s air combat has reached a high level of uncertainty where continuous or discrete variables with crisp values cannot be properly represented using fuzzy sets. With a set of membership functions, fuzzy logic is well-suited to tackle such complex states and actions. However, it is not necessary to fuzzify the variables that have definite discrete semantics.Hence, the aim of this study is to improve the level of model abstraction by proposing multiple levels of cascaded hierarchical structures from the perspective of function, namely, the functional decision tree. This method is developed to represent behavioral modeling of air combat systems, and its metamodel,execution mechanism, and code generation can provide a sound basis for function-based behavioral modeling. As a proof of concept, an air combat simulation is developed to validate this method and the results show that the fighter Alpha built using the proposed framework provides better performance than that using default scripts.

Nonlinear Auto-Companding Method for Behavioral Modeling of Switched-Current Circuits

A wavelet collocation method with nonlinear auto companding is proposed for behavioral modeling of switched current circuits.The companding function is automatically constructed according to the initial error distribution obtained through approximating the input output function of the SI circuit by conventional wavelet collocation method.In practical applications,the proposed method is a general purpose approach,by which both the small signal effect and the large signal effect are modeled in a unified formulation to ease the process of modeling and simulation.Compared with the published modeling approaches,the proposed nonlinear auto companding method works more efficiently not only in controlling the error distribution but also in reducing the modeling errors.To demonstrate the promising features of the proposed method,several SI circuits are employed as examples to be modeled and simulated.

Behavioral modeling of RF power amplifiers with time-delay feed-forward neural networks

A novel behavioral model using three-layer time-delay feed-forward neural networks （TDFFNN）is adopted to model radio frequency （RF）power amplifiers exhibiting memory nonlinearities. In order to extract the parameters, the back- propagation algorithm is applied to train the proposed neural networks. The proposed model is verified by the typical odd- order-only memory polynomial model in simulation, and the performance is compared with different numbers of taped delay lines（TDLs） and perceptrons of the hidden layer. For validating the TDFFNN model by experiments, a digital test bench is set up to collect input and output data of power amplifiers at a 60 × 10^6 sample/s sampling rate. The 3.75 MHz 16-QAM signal generated in the vector signal generator（VSG） is chosen as the input signal, when measuring the dynamic AM/AM and AM/PM characteristics of power amplifiers. By comparisons and analyses, the presented model provides a good performance in convergence, accuracy and efficiency, which is approved by simulation results and experimental results in the time domain and frequency domain.

Non-Overlapping Conditions to Enable Multi- Dimensional Behavioral Models/DPDs for Multi-Band or Non-Continuous Carrier Aggregation Systems

To linearize the multi.band PAs/transmitters, a serial of multi.band predistortion models based on multi.dimensional architecture have been proposed. However, most of these models work properly only for the signals whose harmonic and intermodulation products of carriers' non.overlap with the interested fundamental bands. In this paper, the non.overlapping conditions for dual.band and tri.band signals are derived and denoted in the form of closed.form expression. It can be used to verify whether a given dual.band/multi.band signals can be linearized properly by these multi.dimensional behavioral models. Also the conditions can be used to plan the frequency spacing and maximum bandwidth of a multi.band or non.continuous carrier aggregation signal. Several dual.band and triband signals were tested on the same PA, by employing 2.D DPD and 3.D DPD behavioral models. The measurement results show that the signals which don't satisfy the non.overlapping conditions cannot be linearized well by the multi.dimensional behavioral models which does not take the harmonic and intermodulation products of carriers' into account.

ANIMAL BEHAVIORAL MODELS OF TINNITUS

The pathophysiology of tinnitus is poorly understood and treatments are often unsuccessful. A number of animal models have been developed in order to gain a better understanding of tinnitus. A great deal has been learned from these models re- garding the electrophysiological and neuroanatomical correlates of tinnitus following exposure to noise or ototoxic drugs. Re- liable behavioral data is important for determining whether such electrophysiological or neuroanatomical changes are indeed related to tinnitus. Of the many documented tinnitus animal behavioral paradigms, the acoustic startle reflex had been pro- posed as a simple method to identify the presence or absence of tinnitus. Several behavioral models based on conditioned re- sponse suppression paradigms have also been developed. In addition to determining the presence or absence of tinnitus, some of the behavioral paradigms have provided signs of the onset, frequency, and intensity of tinnitus in animals. Although none of these behavioral models have been proved to be a perfect model, these studies provide useful information on understanding the neural mechanisms underlying tinnitus.

Multislice behavioral modeling based on envelope domain for power amplifiers

An envelope domain multislice behavioral modeling is introduced. The tradition AM-AM and AM- PM characteristics of power amplifiers axe extended to envelope domain and base-band filter is applied to distortion complex envelope signal for description of the envelope memory effect. Using traditional one and two-tone tests, the coefficients of nonlinear model and the FIR filter can be extracted. At last the model has been applied to a 10 W WCDMA Power amplifier to predict its output signal. And simulation results show that the model output conforms very well to the traditional transistor level simulation results.

Study on Volterra-Laguerre behavioral model for RF power amplifier

Voherra series behavioral model for radio frequency （RF） power amplifier （PA） has been widely used in system-level simulation, however, high computational complexity makes this kind of model limited to ＂weak＂ nonlinearity. In order to reduce the computational complexity and the number of coefficients of Volterra series kernels, a Volterra series improved behavioral model based on Laguerre orthogonal polynomials function, namely Voherra-Laguerre behavioral model, is proposed. Mathematical expressions of Volterra-Laguerre behavioral model is derived, and accuracy of the model is verified through comparison of measured and simulation output data from a freescale PA using MRF21030 transistor. Mathematical analysis and simulation results show that Voherra-Laguerre behavioral model has a simple structure, much less coefficients and better modeling performance than general Volterra series model. The model can be used more correctly for system-level simulation of RF PA with wideband signal.

Deep Neural Network Based Behavioral Model of Nonlinear Circuits

With the rapid growth of complexity and functionality of modern electronic systems, creating precise behavioral models of nonlinear circuits has become an attractive topic. Deep neural networks (DNNs) have been recognized as a powerful tool for nonlinear system modeling. To characterize the behavior of nonlinear circuits, a DNN based modeling approach is proposed in this paper. The procedure is illustrated by modeling a power amplifier (PA), which is a typical nonlinear circuit in electronic systems. The PA model is constructed based on a feedforward neural network with three hidden layers, and then Multisim circuit simulator is applied to generating the raw training data. Training and validation are carried out in Tensorflow deep learning framework. Compared with the commonly used polynomial model, the proposed DNN model exhibits a faster convergence rate and improves the mean squared error by 13 dB. The results demonstrate that the proposed DNN model can accurately depict the input-output characteristics of nonlinear circuits in both training and validation data sets.

Behavioral Model of Molecular Gap-Type Atomic Switches and Its SPICE Integration

Atomic switches can be used in future nanodevices and to realize conceptually novel electronics in new types of computer architecture because of their simple structure, ease of operation, stability, and reliability. The atomic switch is a single solid-state switch with inherent learning abilities that exhibits various nonlinear behaviors with network devices. However, previous studies focused on experiments and nonvolatile memory applications, and studies on the application of the physical properties of the atomic switch in computing were nonexistent. Therefore, we present a simple behavioral model of a molecular gap-type atomic switch that can be included in a simulator. The model was described by three simple equations that reproduced the bistability using a double-well potential and was able to easily be transferred to a simulator using arbitrary numerical values and be integrated into HSPICE. Simulations using the experimental parameters of the proposed atomic switch agreed with the experimental results. This model will allow circuit designers to explore new architectures, contributing to the development of new computing methods.

An Improved Behavioral Model for High-voltage and High-power IGBT Chips

High-voltage and high-power IGBT chips have a noticeable carrier storage effect,which is related to the load current.However,the research on the carrier storage effect of existing IGBT behavior models is insufficient.In this paper,An improved behavioral model for high-voltage and high-power insulated gate bipolar transistor(IGBT)chips is proposed,which could be used under different load conditions.The problems for applying the traditional behavioral model to more load conditions are discussed.Carrier behavior,in the wide base region,is analyzed,and the analytical expression of the carrierstorage-effect equivalent capacitance and the initial value of the tail current are provided to establish an improved IGBT behavioral model.A corresponding parameter extraction method is proposed.In order to verify the improved behavioral model,an experimental platform is built for resistive load and inductive load,and the results show that the accuracy of the improved behavioral model is much better than that of the traditional model.In addition,the errors of the improved model are within 12.5%under different current and load types.Considering that the maximum error of other models,which could be applied in a variety of load conditions,is more than 25%,the accuracy of the model proposed in this paper is excellent.

Prospect Theory Based Individual Irrationality Modelling and Behavior Inducement in Pandemic Control

Understanding and modeling individuals’behaviors during epidemics is crucial for effective epidemic control.However,existing research ignores the impact of users’irrationality on decision-making in the epidemic.Meanwhile,existing disease control methods often assume users’full compliance with measures like mandatory isolation,which does not align with the actual situation.To address these issues,this paper proposes a prospect theorybased framework to model users’decision-making process in epidemics and analyzes how irrationality affects individuals’behaviors and epidemic dynamics.According to the analysis results,irrationality tends to prompt conservative behaviors when the infection risk is low but encourages risk-seeking behaviors when the risk is high.Then,this paper proposes a behavior inducement algorithm to guide individuals’behaviors and control the spread of disease.Simulations and real user tests validate our analysis,and simulation results show that the proposed behavior inducement algorithm can effectively guide individuals’behavior.

Observation on the Effect of Parental Participation in Nursing Under the IMCHB Model in Neonatal Hypoxic-Ischemic Encephalopathy

Objective:To investigate the clinical effects of parental participation in nursing under the Interaction Model of Client Health Behavior(IMCHB)model in neonatal hypoxic-ischemic encephalopathy(HIE).Methods:The First Affiliated Hospital of Gannan Medical University included 46 newborns with HIE admitted from October 2021 to October 2023 into the study population.They were divided into a control group and an observation group according to the random number table method,with the control group adopting routine nursing,and the observation group implementing parental participation in nursing under the IMCHB model.The indicators of physical,intellectual,and psychomotor development of the two groups were compared before and after nursing.Results:The physical,intellectual,and psychomotor development of the observation group was higher than that of the control group after 3 months of nursing,and the difference was statistically significant(P<0.05).Conclusion:The implementation of the IMCHB model of parental participation in the clinical care of HIE neonates can further promote their physical,intellectual,and psychomotor development.

Simulink Behavioral Modeling of a 10-bit Pipelined ADC

The increasing architecture complexity of data converters makes it necessary to use behavioral models to simulate their electrical performance and to determine their relevant data features. For this purpose, a specific data converter simulation environment has been developed which allows designers to perform time-domain behavioral simulations of pipelined analog to digital converters （ADCs）. All the necessary blocks of this specific simulation environment have been implemented using the popular Matlab simulink environment. The purpose of this paper is to present the behavioral models of these blocks taking into account most of the pipelined ADC non-idealities, such as sampling jitter, noise, and operational amplifier parameters （white noise, finite DC gain, finite bandwidth, slew rate, and saturation voltages）. Simulations, using a 10-bit pipelined ADC as a design example, show that in addition to the limits analysis and the electrical features extraction, designers can determine the specifications of the basic blocks in order to meet the given data converter requirements.

Behavioral Model of Decision-Making in Economic Systems

A behavioral model of decision-making is presented to obtain the optimal solution of some economic systems. A numerical example is discussed in detail with what-if and what-for analysis to illustrate the advantages of this model.

用于学习和解释行人预期行为的群体交互场

Anticipating others’actions is innate and essential in order for humans to navigate and interact well with others in dense crowds.This ability is urgently required for unmanned systems such as service robots and self-driving cars.However,existing solutions struggle to predict pedestrian anticipation accurately,because the influence of group-related social behaviors has not been well considered.While group relationships and group interactions are ubiquitous and significantly influence pedestrian anticipation,their influence is diverse and subtle,making it difficult to explicitly quantify.Here,we propose the group interaction field(GIF),a novel group-aware representation that quantifies pedestrian anticipation into a probability field of pedestrians’future locations and attention orientations.An end-to-end neural network,GIFNet,is tailored to estimate the GIF from explicit multidimensional observations.GIFNet quantifies the influence of group behaviors by formulating a group interaction graph with propagation and graph attention that is adaptive to the group size and dynamic interaction states.The experimental results show that the GIF effectively represents the change in pedestrians’anticipation under the prominent impact of group behaviors and accurately predicts pedestrians’future states.Moreover,the GIF contributes to explaining various predictions of pedestrians’behavior in different social states.The proposed GIF will eventually be able to allow unmanned systems to work in a human-like manner and comply with social norms,thereby promoting harmonious human-machine relationships.

An Envelope Hammerstein Model for Power Amplifiers

In this paper, an envelope Hammerstein （EH） model is introduced to describe dynamic input -output characteristics of RF power amplifiers. In the modeling approach, we use a new truncation method and an established nonlinear time series method to determine model structure. Then, we discuss the process of model parameter extraction in detailed. Finally, a 2 W WCDMA power amplifier is measured to verify the performance of EH model, and good agreement between model output and measurement result shows our model can accurately predict output characteristic of the power amplifier.

Symbolic Macromodeling for Statistical Simulation of Operational Amplifiers

Symbolic circuit simulator is traditionally applied to the small-signal analysis of analog circuits. This paper establishes a symbolic behavioral macromodeling method applicable to both small-signal and large-signal analysis of general two-stage operational amplifiers （op-amps）. The proposed method creates a two-pole parametric macromodel whose parameters are analytical functions of the circuit element parameters generated by a symbolic circuit simulator. A moment matching technique is used in deriving the analytical model parameter. The created parametric behavioral model can be used for op-amps performance simulation in both frequency and time domains. In particular, the parametric models are highly suited for fast statistical simulation of op-amps in the time-domain. Experiment results show that the statistical distributions of the op-amp slew and settling time characterized by the proposed model agree well with the transistor-level results in addition to achieving significant speedup.

Modeling for Ethernet passive optical network receiver

A behavior model for the receiver of the Ethernet passive optical network（EPON） is presented. The model consists of a fiber, a photodetector, a transimpedance amplifier （TIA） followed by a limiting amplifier and a clock and data recovery＇ circuit （CDR）. Each sub-model is constructed based on the architecture of a circuit. The noise and jitter in each block such as shot noise, thermal noise, deterministic and random jitter are also considered. The performance of the whole receiver can be evaluated by the simulation of the behavior model, which is faster than the ordinary circuit model and more accurate than the analytical model. The whole model is implemented with C ＋＋ and simulated in Microsoft Visual C ＋＋ 6. 0. Using the Monte Carlo method, the EPON receiver is simulated. The simulation results show a good agreement with experimental ones.

GLOBAL EXISTENCE AND ASYMPTOTIC BEHAVIOR FOR AN 1-D COMPRESSIBLE ENERGY TRANSPORT MODEL

In this article, the global existence and the large time behavior of smooth solutions to the initial boundary value problem for a degenerate compressible energy transport model are established.

Lewin Behavior Model-Based Study on Influence Factors of Individuals to Mobile Service Demand

How to fmd main influence factors of individuals to mobile service demand is investiga- ted. The empirical research is conducted in the sample of high-value customers in China mobile market. Based on Lewin behavior model, this pa- per establishes factors-matrix from personal and environmental dimensions. Relationships among multiple factors are tested in the structural equa- tion model and their impacts on customers＇ de- mands are elaborated. Findings indicate that opera- tional convenience and business brand image have significant effects on sample users＇ demands. Fur- thermore, annual income, gender, occupation, the needs of access to information and the needs of enriching and improving social relationships are al- so important factors for high-value users. The re- suits may provide further insights into mobile service demand and the model can be popularized to other behavior researches.