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首页> 外文会议>International Conference on Management, Manufacturing and Materials Engineering >Simulation and Optimization of a Biscuit Processing Production in an Industrial Scale by use of MLP and RBF Neuro Fuzzy Network
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Simulation and Optimization of a Biscuit Processing Production in an Industrial Scale by use of MLP and RBF Neuro Fuzzy Network

机译：利用MLP和RBF神经模糊网络模拟和优化工业规模饼干加工生产

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An industrial process of corn biscuit manufacture had been simulated and optimized by RBF and MLP networks, in this work. Data bases of the cooking process, in continuous, indirect, gas-fired oven (industrial scale) were used to the best understanding the behavior of the control variables. Weight and thickness of a corn biscuit were used as responses. After training, neuro Fuzzy model was applied on experimental data. Results showed that Neuro Fuzzy network has a good response surface, for both RBF and MLP networks; it showed low values of average error. The RBF and MLP network architectures showed satisfactory results, in line with the experimental data. Optimization showed that is possible to obtain one biscuit with 4.64-5.00 g and 5.14-5.65 mm of thickness and wrapping up in a package of 400 g of net weight. This allows the maintenance of the product of the damaged norms of quality.
机译：在这项工作中，已经通过RBF和MLP网络进行了模拟和优化了玉米饼干制造的工业过程。烹饪过程的数据基础，连续，间接，燃气烤箱（工业规模）用于最佳理解控制变量的行为。玉米饼干的重量和厚度用作反应。培训后，神经模糊模型应用于实验数据。结果表明，对于RBF和MLP网络，神经模糊网络具有良好的响应面;它显示出平均误差的低值。 RBF和MLP网络架构表现出令人满意的结果，符合实验数据。优化表明，可以获得厚度为4.64-5.00g和5.14-5.65mm的一个饼干，并以400g净重的包装包装。这允许维护损坏质量规范的产品。

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