PFTL 101B-20.0kN 3BSE004203R1 | ABB | Feedforward neural network hardware

Description

PFTL 101B-20.0kN 3BSE004203R1 | ABB | Feedforward neural network hardware

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When PFTL 101B-20.0kN  practical neural network application systems, hardware implementation issues must be considered. High performance specialized neural network hardware for specific applications is the ultimate goal of neural network research. Therefore, FPGA devices are selected for this work, and a data-driven pulse array parallel processing method is used to design a three-layer (4-6 3) circuit consisting of 13 neurons. Due to the intuitive design of the electrical schematic, Therefore, an electrical schematic design approach was adopted at the top level, while the functional modules were designed using VHDL, a descriptive approach

PFTL 101B-20.0kN  should be pointed out that the transfer function obtained after linearization is an approximate mathematical model of the controlled object, and the coefficients are slowly time-varying, which can be used as the design basis for decoupling controllers (classical transfer functions, Nyquist methods, etc.). For the vast majority of cases, the gain of the decoupler should not be constant. If optimization is to be achieved, the decoupler must be nonlinear, even adaptive. If the decoupler is linear and steady, So it can be expected that decoupling will be incomplete. In some cases, the error of the decoupler may cause instability. In this paper, a PID decoupling controller tuned by BP neural network was used for simulation research, and the specific algorithm is shown in the literature

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