This chapter utilizes the direct neural control (DNC) based on back propagation neural networks (BPN) with specialized learning architecture applied to the speed control of DC servo motor. The proposed neural controller can be treated as a speed regulator to keep the motor in constant speed, and be applied to DC servo motor speed control. The proposed neural control applied to position control ...

The Doctoral Programme in Water Engineering is coordinated by the Department of Hydraulic, Environmental, Infrastructures, and Surveying Engineering (DIIAR). It offers technical/scientific curricula geared to the design, planning and management of sustainable development strategies related to the interactions between human life and environment in all aspect involving water. The programme is foc...

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....

Prognostic is a key step of the condition-based maintenance (CBM). In order to improve the predicting performance, a novel method for prognostic for the hydraulic pump is proposed in this paper. Based on the improvement of the traditional composite spectrum, the DCT-composite spectrum (DCS) fusion algorithm is initially presented to make fusion of multi-channel vibration signals. The DCS compos...

The implementation of a Neuro-Fuzzy nonlinear adaptive structure, with Local Linear Models (LLM), designed for fuel pressure estimation in diesel common-rail (CR) hydraulic system, represents the main topic. Hydraulic systems, in general, are nonlinear and engineers have often struggled to find the best solution to approximate the input-output dependencies. Powerful tools are necessary for spli...

The clear understanding of hydraulic fracture network complexity and the optimization of fracture network configuration are important to the hydraulic fracturing treatment of shale gas reservoirs. For the prediction of hydraulic fracture network configuration, one of the problems is the accurate representation of natural fractures. In this work, a real natural fracture network is reconstructed ...