Applications of neural networks to simulate soil-tool interaction and soil behavior
Authors: Z.X. Zhang and R.L. Kushwaha
Description: Principles of radial basis function (RBF) neural network and its application procedure have been described using simulation of (I) tool draft at high operating speed, (2) frozen soil strength behavior associated with factors such as confining pressure, strain rate, and soil temperature, and (3) soil freezing and thawing processes. In the first illustrative example, the relation between draft and operating speed, tool and soil types were established using a RBF network. In the second example the relationship between frozen soil strength and confining pressure, strain rate, soil dry density, and soil temperature were depicted. The final example was to predict soil temperature as a function of air temperature, elapsed time, and depth during soil freezing and thawing processes. These illustrative examples indicated that the RBF network is a powerful tool for simulating highly nonlinear systems that are difficult to describe using analytical methods.
Citation: Z.X. Zhang and R.L. Kushwaha 1999. APPLICATIONS OF NEURAL NETWORKS TO SIMULATE SOIL-TOOL INTERACTION
AND SOIL BEHAVIOR. Canadian Agricultural Engineering 41(2):119-125.
Publisher: Canadian Society for Bioengineering
Language 1: en
Rights: Canadian Society for Bioengineering