Error calibration of pitching mechanism motion based on improved ant colony algorithm

Abstract

A method for calibrating the motion error of the pitching mechanism based on an improved ant colony algorithm is proposed in this article. First, the error model for the pitching mechanism is built based on the three main error sources that affect the accuracy of the pitching mechanism, and the influence of these three error sources on the pitch motion accuracy is analyzed using the control variable method. Second, a mathematical model for the calibration of the pitch mechanism motion error is established, which transforms the error calibration problem into an optimization problem with multiple objective functions, and the error is then analyzed using an improved ant colony algorithm model. Compared with traditional numerical methods and ant colony optimization, it is proven that the algorithm has strong global optimization ability and can effectively avoid the impact of the initial value error source on the calculation results, and the accuracy can reach a level of 10−5 mm. Finally, the simulation results show that the proposed method is efficient and practical.

Optimization Simulation of English Speech RecognitionAccuracy Based on Improved Ant Colony Algorithm

“…e extraction of speech feature parameters is to extract the parameters that can effectively represent the speaker's speech features, including the height of the formant frequency, the size of the bandwidth, the fundamental frequency, spectrum, and other parameters [19]. It is difficult to separate and extract these feature parameters 4 Complexity accurately [20][21][22]. Based on the study of human voice mechanism and auditory perception of human ear, researchers have proposed a variety of speech feature parameters for speaker recognition, which mainly include the following categories [23][24][25]:…”

“…Ant colony algorithm has many excellent characteristics and is easy to combine with other methods. In the speech recognition system based on template matching, the time warping algorithm compares the reference template with the test template, maps it on a plane, and searches an optimal path from the starting point to the end point according to certain criteria [4]. is is a local optimization algorithm, each step of the search is based on the judgment of local optimization, and the ant colony algorithm can just solve the defect that the time planning path cannot reach the global optimization, which also provides the feasibility for the application of ant colony algorithm in speech recognition.…”

A hybrid optimization approach to forecast automobile sales of China

“…The amount of the pheromone deposited on the ground completely relies on the quality and quantity of the source discovered. [25][26][27][28] The quantity of the pheromone (tt) is intensified around the best objective value obtained throughout the simulation run in the continuous ACO algorithm. The location of the kth ant in the solution space is presented in the following equation…”