In this paper, a predictive control strategy based on neuro-fuzzy model is applied to Continuous Stirred Tank Reactor (CSTR) process, which has characteristic of highly nonlinearity.
针对具有高度非线性特性的连续搅拌反应釜(CSTR)控制过程，研究了基于神经模糊模型的预测控制策略。
Then two different fuzzy systems are designed to approximate the direct model and the inverse one on the basis of adaptive neuro-fuzzy inference system(ANFIS).
根据自适应神经模糊推理系统原理，设计两个模糊系统分别逼近磁流变阻尼器的正模型和逆模型。
The control for speed limit on expressway is a nonlinear and time variable system, it is difficult to simulate with a mathematical model. A neuro-fuzzy network is proposed to solve the problem.
高速公路限速控制是一个非线性时变系统，难于用数学模型准确建模，提出一种模糊神经网络实现限速控制。
The subsidence of the indoor model test is also predicted with this theory. The observed data are compared with the predicted data with the adaptive neuro-fuzzy inference system (ANFIS).
对室内模型试验进行沉降预测，并和实验观测数据以及自适应神经网络系统(ANFIS)预测结果进行了比较。
The model is used to perform the numerical simulation of slope stable state, to acquire the data for adaptive neuro-fuzzy inference system(ANFIS) analysis.
同时基于自适应神经模糊推理系统建立了岩体力学参数与边坡抗滑力和下滑力的映射模型，分析得到抗滑力和下滑力的统计特征。
This paper presents a predictive coding model based on adaptive neuro-fuzzy inference system (ANFIS).
提出了一种利用神经模糊推理系统（ANFIS）构建预测器的图像压缩预测编码算法。
This paper presents a predictive coding model based on adaptive neuro-fuzzy inference system (ANFIS).
提出了一种利用神经模糊推理系统（ANFIS）构建预测器的图像压缩预测编码算法。
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