小波神经网络 Resources

Showing items tagged with "小波神经网络"

In maneuvering target tracking, the target motion model serves as a fundamental component that ideally captures various movement states during target maneuvers. Commonly used models include Constant Velocity (CV) model, Constant Acceleration (CA) model, time-correlated Singer model, and the "Current" Statistical model for maneuvering targets. These models characterize target maneuvers using a maneuver frequency parameter. In practical applications, a fixed maneuver frequency is typically employed, implying constant maneuver duration. However, actual target maneuver durations vary continuously, meaning the maneuver frequency changes dynamically. Using a fixed maneuver frequency inevitably introduces tracking errors. When the sampling period ranges from 0.5 to 2 seconds, lower maneuver frequencies yield higher tracking accuracy [1]. This description highlights the need for adaptive frequency adjustment algorithms that can dynamically optimize tracking performance through neural network implementations.

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Establishing a wavelet neural network load forecasting model with optimized node selection (input layer, hidden layer, output layer) and implementing appropriate training functions to enhance convergence speed and forecasting accuracy. Includes algorithm configuration and parameter tuning strategies.

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Time series data represents sequences that change randomly over time, where forecasting involves using historical data points to predict current and future values. Traditional time series prediction methods in stochastic process theory typically employ linear models such as AR, MA, and ARMA models. However, these models require manual selection of model types and determination of orders, often resulting in significant prediction errors. Wavelet theory, an emerging mathematical method, has gained prominence in recent years. Wavelet neural networks combine wavelet analysis with neural networks to effectively address time series prediction challenges. This case study demonstrates the application of wavelet neural networks for traffic flow prediction, highlighting their effectiveness in time series forecasting through practical implementation and algorithm validation.

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