Research on Adaptive Linear Neuron Method for Frequency Stability of Magnetron in Electronic Linear Accelerator

1、 Introduction

Linear Accelerator (Linac) has a wide range of applications in scientific research, medical diagnosis, industrial production, and other fields. During the operation of an accelerator, the magnetron, as one of its core components, is responsible for generating and adjusting the accelerating magnetic field of the electron beam. Its frequency stability directly affects the overall performance of the accelerator. However, due to external interference, temperature changes, component aging, and other factors, the frequency of magnetrons often fluctuates, leading to a decrease in accelerator performance. Therefore, how to achieve stable control of magnetron frequency has become one of the hot topics in current accelerator technology research.
In recent years, with the rapid development of artificial intelligence and neural networks, adaptive control methods have achieved significant results in multiple fields. This article proposes a frequency stabilization control method for electron linear accelerator magnetron based on Adaptive Linear Neuron (ALN). This method constructs an adaptive linear neuron model to monitor the frequency fluctuations of the magnetron in real time, and automatically adjusts control parameters based on the monitoring results, achieving stable control of the magnetron frequency. This article will provide a detailed explanation of the method and verify its effectiveness through experiments.


2、 The basic principles of magnetrons in electronic linear accelerators

The electron linear accelerator accelerates electrons from a stationary state to near the speed of light through a series of electric and magnetic fields. Among them, the magnetron is a key component for generating and regulating the accelerating magnetic field. Magnetron is usually composed of ferromagnetic materials and coils, which can generate magnetic fields of different strengths and directions by changing the magnitude and direction of the current in the coil. These magnetic fields interact with the electron beam, allowing it to gain energy during the acceleration process.

However, in actual operation, the frequency of the magnetron often fluctuates due to external interference, temperature changes, component aging, and other factors. These fluctuations can cause instability in the accelerating magnetic field, which in turn affects the energy distribution and acceleration efficiency of the electron beam. Therefore, effective control methods need to be adopted to achieve stable control of magnetron frequency.

3、 Overview of Adaptive Linear Neuron Methods

Adaptive linear neuron is an adaptive control method based on neural networks. It achieves real-time processing and control of input signals by simulating the structure and function of biological neurons. Adaptive linear neurons have self-learning ability and can automatically adjust internal parameters according to changes in input signals to adapt to different control requirements.
In the frequency stability control of the magnetron in an electronic linear accelerator, adaptive linear neurons can serve as controllers to monitor the frequency fluctuations of the magnetron in real time and automatically adjust control parameters based on the monitoring results. Specifically, adaptive linear neurons can receive real-time monitoring data of magnetron frequency as input signals, and through internal calculation and processing, output corresponding control signals to the magnetron coil to adjust the current size and direction in the coil, thereby stabilizing the frequency of the magnetron.

4、 Adaptive linear neuron model construction

In order to achieve the application of adaptive linear neurons in frequency stability control of magnetrons in electronic linear accelerators, a corresponding model needs to be constructed. This model includes input layer, output layer, and weight coefficients connecting the two layers.

Input layer: The input layer receives real-time monitoring data of magnetron frequency as the input signal. In order to improve control accuracy and stability, multiple monitoring points can be used to simultaneously monitor the frequency of the magnetron, and these monitoring data can be input as input signals into the adaptive linear neuron model.

Output layer: The output layer outputs the corresponding control signal to the magnetron coil. Based on the changes in the input signal, the adaptive linear neuron calculates the corresponding control signal and transmits it to the magnetron coil through the output layer.

Weight coefficient: The weight coefficient is a parameter that connects the input layer and the output layer, used to adjust the degree of influence of the input signal on the output signal. In adaptive linear neuron models, weight coefficients can be automatically adjusted through learning algorithms. When the input signal changes, the adaptive linear neuron will automatically adjust the weight coefficients based on the current state and error signal to optimize the accuracy and stability of the output signal.


5、 Design of adaptive learning algorithms

In order to achieve the self-learning ability of adaptive linear neurons, it is necessary to design corresponding adaptive learning algorithms. This algorithm is used to automatically adjust the weight coefficients based on the error signal between the input signal and the output signal to optimize the accuracy and stability of the output signal.

Common adaptive learning algorithms include gradient descent, Least Mean Square (LMS), and so on. In this article, we choose the LMS algorithm as the adaptive learning algorithm. The LMS algorithm is an iterative algorithm based on the minimum mean square error criterion. It evaluates the performance of the current weight coefficients by calculating the sum of squares of the error signal, and adjusts the weight coefficients along the negative gradient direction of the error signal to reduce the error signal.

In the LMS algorithm, it is necessary to set parameters such as learning rate and step size to control the convergence speed and stability of the algorithm. The selection of these parameters should be adjusted according to specific application scenarios and control requirements.

6、 Experimental verification and result analysis

In order to verify the effectiveness of the frequency stability control method for electron linear accelerator magnetron based on adaptive linear neurons proposed in this article, we conducted experimental verification. In the experiment, we constructed a simulated electron linear accelerator system and implemented an adaptive linear neuron controller in the system.

The experimental results show that the electronic linear accelerator system using an adaptive linear neuron controller has higher frequency stability. Under the influence of external interference, temperature changes, component aging, and other factors, the frequency fluctuation of the magnetron has been effectively controlled, and the energy distribution and acceleration efficiency of the electron beam have also been significantly improved.