Study of Tractor AMT Automatic Gear Shift Based on Artificial Neural Network
نویسنده
چکیده
The paper aims at the complex situation of the tractor field operation and adopts the gear shift rule of two parameters to control the six gear shift of the tractor. Using the artificial neural network chip the model number of which is ZISC036 and the computer on slice the model number of which is PIC17C42 establishes the controller of the radial basis function artificial neural network-RBFANN. Based on the optimal gear shift information emitted by the RBFANN controller, PIC17C4 emits the control command to control the hydraulic system to realize the automatic gear shift according to the predetermined rule. The sample data is simulated by MATLAB. It shows that RBFANN identification system can solve the problem of the gear recognition and the gear position can be accurately identified. The paper offers the theoretical basic for the design and study of the tractor AMT automatic gear shift. Copyright © 2014 IFSA Publishing, S. L.
منابع مشابه
Design, Development and Evaluation of an Orange Sorter Based on Machine Vision and Artificial Neural Network Techniques
ABSTRACT- The high production of orange fruit in Iran calls for quality sorting of this product as a requirement for entering global markets. This study was devoted to the development of an automatic fruit sorter based on size. The hardware consisted of two units. An image acquisition apparatus equipped with a camera, a robotic arm and controller circuits. The second unit consisted of a robotic...
متن کاملDevelopment of Intelligent Gear-Shifting Map Based on Radial Basis Function Neural Networks
Currently, most automobiles have automatic transmission systems. The gear-shifting strategy used to generate shift patterns in transmission systems plays an important role in improving the performance of vehicles. However, conventional transmission systems have a fixed type of shift map, so it may not be enough to provide an efficient gear-shifting pattern to satisfy the demands of driver. In t...
متن کاملNeural Network-Based Learning Kernel for Automatic Segmentation of Multiple Sclerosis Lesions on Magnetic Resonance Images
Background: Multiple Sclerosis (MS) is a degenerative disease of central nervous system. MS patients have some dead tissues in their brains called MS lesions. MRI is an imaging technique sensitive to soft tissues such as brain that shows MS lesions as hyper-intense or hypo-intense signals. Since manual segmentation of these lesions is a laborious and time consuming task, automatic segmentation ...
متن کاملStep change point estimation in the multivariate-attribute process variability using artificial neural networks and maximum likelihood estimation
In some statistical process control applications, the combination of both variable and attribute quality characteristics which are correlated represents the quality of the product or the process. In such processes, identification the time of manifesting the out-of-control states can help the quality engineers to eliminate the assignable causes through proper corrective actions. In this paper, f...
متن کاملBedload transport predictions based on field measurement data by combination of artificial neural network and genetic programming
Bedload transport is an essential component of river dynamics and estimation of its rate is important to many aspects of river management. In this study, measured bedload by Helley- Smith sampler was used to estimate the bedload transport of Kurau River in Malaysia. An artificial neural network, genetic programming and a combination of genetic programming and a neural network were used to estim...
متن کامل