Research On The Method Of Coal_Rock Interface Recognition Based On Genetic Neural Network

Coal-rock interface recognition(CIR) can make the shearer have the ability to automatically trace the coal-rock interface. It can not only contribute to mine automation and high efficiency, but also reduce the content of rock and the other mineral that must be removed in the process of coal beneficiation. It is one of the key techniques of mining automation.The present methods collect information by the single sensor only. Because every single sensor has its own special certain precision and application range, collecting information by the single sensor is limited to a certain area and mistakes identification sometimes unavoidable.Researching and developing an identification system uses mufti-sensor is a tendency in order to provide design base for the height adjustment system. Due to the above consideration, the paper has proposed a totally new coal-rock interface recognition method based on multi-sensor data fusion technique with multi-sensor collecting response signal of cutting force. The method can enhance precision and accuracy greatly with multi-signal data fusion technique.In this paper, CIR testing system is established, The control system is manufactured, and test scheme is decided. Through the test, volumes of testing data that are taken as information resource have collected.Feature extraction is carried out with wavelet packet technique because of its advantages. The sensitive frequency bands of signals are judged and their features can be extracted by the energy method. Feature extraction with wavelet packet technique finishes the conversion of pattern space to extraction space, filtrates the factors that cannot reveal sample essentials. It compresses greatly feature dimensionality, acquires the characteristic variables which can reveal sample attributive. It provides correct and reliable feature level data for data fusion.Data fusion, as a cross synthetic information process technique, presents power predominance of carrying out coal-rock interface recognition. The paper probes the advantage of information fusion with the genetic neural network technique.In this paper, to the question, back propagation network are easily running into local infinitesimal ,genetic algorithms is used to optimize feedforward neural networks and establishs the genetic neural network model.For the CIR problem, the structure and training algorithm of the genetic neural network are decided:I .Choose real-coded pattern, shortening the length of coded individual, and code neural network's threshold value and weights simultaneously.II .Design fitness function which can accurately indicate neural networks' performance, at the same time neural network's structure should not be neglected.III.Design and improve genetic operators which adapt to real-coded genetic algorithm,avoiding prematurity.The results show that the test data can be used to train and simulate with the neural network.System adopted the genetic neural network technique can carry out the CIR and the system adopted the modified algorithm has the higher identification ratio which is more than 90%. The method of CIR based on mufti-sensor data fusion technique is feasible and more reliable.