应用背景 Resources

Showing items tagged with "应用背景"

Development Background Tabu Search (TS), first proposed by Glover in 1986, extends local neighborhood search as a global stepwise optimization algorithm that simulates human intellectual processes. Key Technology The TS algorithm employs a flexible memory structure and corresponding tabu criteria to avoid cyclical searches, while incorporating aspiration criteria to override tabu restrictions for promising solutions. This ensures diversified exploration and ultimately achieves global optimization. Compared to simulated annealing and genetic algorithms, TS represents another meta-heuristic approach with distinct search characteristics. To date, TS has achieved significant success in combinatorial optimization, production scheduling, machine learning, circuit design and neural networks.

MATLAB 234 views Tagged
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Application Background The official name of 3G LTE is 3G Long Term Evolution (LTE), which is part of the 3GPP Long Term Evolution project. The LTE system incorporates key technologies such as OFDM (Orthogonal Frequency Division Multiplexing) and MIMO (Multi-Input & Multi-Output), significantly improving spectrum efficiency and data transmission rates. Key Technologies This LTE simulation model is highly practical and can quickly help users understand LTE link-level operations. Several functionalities are implemented using C code, and the model comprehensively covers most existing LTE technologies. MATLAB integration enables efficient simulation and analysis of OFDM and MIMO implementations within LTE systems.

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Application Context: The Traveling Salesman Problem (TSP) is a classic NP-hard combinatorial optimization problem where a salesman must visit n cities exactly once and return to the origin while minimizing total travel distance. Particle Swarm Optimization (PSO), introduced by psychologist James Kennedy and electrical engineer Russell Eberhart in 1995, is a global optimization algorithm. This study employs a modified PSO algorithm enhanced with swap operators and swap sequences to solve discrete TSP problems, demonstrating advantages in implementation simplicity, high accuracy, and rapid convergence.

MATLAB 202 views Tagged
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Application Context: Channel modeling in communication systems, performing LS channel estimation via CE-BEM. This approach provides a simpler implementation compared to previously shared code. Key Technology: Utilizing CE-BEM model for time-varying channel estimation with enhanced computational efficiency.

MATLAB 216 views Tagged
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Application Background When BP networks are used for function approximation, the weight adjustment employs negative gradient descent method which has limitations such as slow convergence and local minima. Radial Basis Function (RBF) networks outperform BP networks in approximation capability, classification performance, and learning speed. MATLAB provides four radial basis function-related functions that create two-layer neural networks, with the first layer being radial basis layers and the second layer being either linear or competitive layers. The primary differences lie in their weight/threshold calculation methods and the presence of thresholds. Key Technology RBF networks can approximate arbitrary nonlinear functions, handle complex system patterns that are difficult to analyze analytically, demonstrate excellent generalization capability, and achieve rapid learning convergence speeds.

MATLAB 189 views Tagged
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Application Context: GPS technology is extensively utilized across daily life, scientific research, and industrial applications. This program performs GPS signal acquisition and satellite searching by determining visible satellite PRN numbers, Doppler frequency shifts, and initial code phases. The results obtained can be further utilized for signal tracking and navigation solution computation. Key Technologies: ca_repeat.m generates sampled sequences of specified C/A codes, CAENCODE.m produces C/A code sequences, findmax.m identifies peak values in correlation results, and GPSAcq.m serves as the main acquisition function. The program allows customization of GPS signal parameters including frequency points, intermediate frequency, and sampling rate during initialization. The code package includes real GPS datasets for practical testing and validation.

MATLAB 207 views Tagged
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Application Background: This program implements the Particle Swarm Optimization (PSO) algorithm to optimize Least Squares Support Vector Machine (LSSVM) parameters. Key Technologies: PSO heuristic optimization, LSSVM machine learning, hyperparameter tuning, classification and regression tasks.

MATLAB 178 views Tagged
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Application Background: Digital recognition represents a crucial research direction in the pattern recognition field with broad application prospects. Based on fundamental principles of BP neural networks, this paper proposes a handwriting digit recognition solution utilizing BP neural network methodology. Key Technology: The core concept of the BP algorithm involves a learning process consisting of two phases: forward propagation of signals and backward propagation of errors. During forward propagation, input samples pass through the input layer, undergo progressive processing through hidden layers, and transmit to the output layer. If discrepancies exist between actual outputs and expected outputs (teacher signals), the system initiates the backward error propagation phase.

MATLAB 211 views Tagged
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Application Background: Implementation of Permanent Magnet Synchronous Motor (PMSM) using powerful simulation modeling capabilities through MATLAB/Simulink. The complete PMSM control system is divided into independent functional modules including PMSM main body module, inverter module, coordinate transformation module, SVPWM module, and production modules. Key Technologies: PMSM simulation modeling and vector control implementation using MATLAB/Simulink's block-based approach for system design and analysis.

MATLAB 196 views Tagged
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Application Background Power spectrum, short for power spectral density function, is defined as signal power per unit frequency band. It indicates how signal power changes with frequency, representing the distribution of signal power in the frequency domain. Key Technologies Power spectral density provides a power description method distinct from time-domain analysis, commonly used in statistical signal processing. Two fundamental applications are introduced: (1) Definition of white noise and colored noise. If a signal's power spectrum equals a constant, the random process is called white noise; otherwise, it's termed colored noise. (2) Calculating signal's autocorrelation function using its relationship with autocovariance function.

MATLAB 221 views Tagged
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