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Process modeling plays a very important role in process system engineering. However, it is more and more difficult to develop an accurate model for a complex chemical process due to the highly nonlinear and complicated process data. For the purpose of handling this problem, an improved functional link learning machine using singular value decomposition (SVD-FLLM) is presented to develop accurate models for complex chemical processes. In the proposed SVD-FLLM model, singular value decomposition is adopted to reduce the expanded variable dimension. Then, the least square algorithm is used to build a regression model between the reduced outputs and the expected vectors. For the sake of validating the testing effectiveness of the proposed SVD-FLLM method, a UCI dataset named Airfoil Self-Noise is first selected. Then the proposed SVD-FLLM is used to develop a model for an actual complex chemical system. Simulation results indicate that, compared the traditional functional link neural network (FLNN), the improved SVD-FLLM can achieve higher accuracy and faster convergence. © 2019 IEEE.
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