神经网络和模糊逻辑简化

M. Masri
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This paper will bring both of these technologies to an understanding level and will show how the combination of both technologies, NeuFuz, takes advantage of both Neural Networks and Fuzzy Logic by eliminating their drawbacks, thus providing the design engineer with a very simple tool that reduces design time and provides a more effective solution. A design of a fan controller will be discussed as an application of this technology. 1.0 Introduction Control applications today demand more and more intelligence and flexibility. These requirements demand for a control system that can handle non linearity and time variation. Moreover the control system should be simple to understand and easy to develop. Conventional techniques are not well suited for nonlinear time variant applications. Neural networks is an elegant solution for nonlinear applications. It requires a number of data sets representing the output as a function of the inputs, covering the entire range of operation. 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Both of these technologies have some shortcomings, but the combination of the two offers an excellent solution that is made simple, and is based on the advantages of both, alleviating the drawbacks. In addition, this solution automatically generates code that implements the application in either COP800 specific assembly code or in ANSI C language. 2.0 The Combined Neural network Fuzzy Logic Solution (NeuFuz): As discussed in the introduction, both technologies neural networks and kzzy logic have many advantages and some disadvantages. The combined solution takes advantage of the neural networks learning capability and applies it to resolve the membership functions and rules generation of the Fuzzy logic, therefor coming up with an integrated solution that offers a tremendous tool to the design engineer. The most significant advantage of neural networks, is the ability of the net to learn the system behavior of the application. A process in which the engineer applies a number of data points representing the output as a function of the inputs selected in the control application. The data points used to train the net are a result of the designer’s knowledge of the system’s expected fbnctionality and determined by the system requirements, measurements or simulations. The data points are also a partial representation of the control surface of the system. The neural network has the ability to generalize and determine the output for any input value applied. The training process of the neural network is applying the input values to the net and adjusting the weights of the neuron connections between the layers to get the appropriate output value to the required accuracy of the system. The trained network and internal weight distribution can now be mapped into membership fimctions and kzzy rules. 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引用次数: 0

摘要

工程师和科学家一直在努力模仿人类大脑的学习过程,这是一种概括和适应变化的能力。神经网络技术就是基于这一目标而开发的,目前正被用于实时控制应用中,得益于这些先进的功能。另一方面,模糊逻辑是一种解决问题的技术,它使用人类语言(如英语)中使用简单规则对系统进行近似描述。乍一看,这些技术似乎难以理解,在实际应用程序中难以使用。本文将把这两种技术带到一个理解水平,并将展示如何结合这两种技术,NeuFuz,利用神经网络和模糊逻辑,消除它们的缺点,从而为设计工程师提供一个非常简单的工具,减少设计时间,并提供更有效的解决方案。作为该技术的一个应用,本文将讨论一个风扇控制器的设计。当今的控制应用对智能化和灵活性的要求越来越高。这些要求要求控制系统能够处理非线性和时变。此外,控制系统应简单易懂,易于开发。传统的方法不适用于非线性时变应用。神经网络是非线性应用的一种优雅的解决方案。它需要一些数据集,将输出表示为输入的函数,覆盖整个操作范围。这些数据被用来训练神经网络。一旦训练完成,神经网络就可以泛化并确定任何输入的输出。神经网络是由相互连接的神经元组成的网络,其中每个神经元都有能力根据前一阶段连接到它的神经元的所有输入信号来计算输出信号。然后将输出信号乘以相应的权重(连接强度),然后再传输到下一阶段的神经元。神经网络的权重,或者换句话说,神经元之间的连接强度是在一个叫做学习的过程中确定的,ISBN# 0-7803-2636-9 596训练神经网络以达到特定应用所需的精度水平的过程。虽然该技术可以很好地处理非线性系统需求,但实现起来往往很昂贵。模糊逻辑是另一种被证明非常成功的技术,它可以解决许多领域的非线性问题,在这些领域中,系统的数学建模很难实现或成本很高。模糊逻辑在这些设计案例中提供了简单和易于设计的功能。然而,模糊逻辑也存在一些问题:模糊逻辑的过程是确定输入和输出的隶属函数,并为应用程序生成一些规则。这里的一个重要因素是,隶属函数和规则对输出有实质性的影响,从而对控制系统的运行有实质性的影响。当系统的复杂性增加时,难以生成隶属函数集和规则集。此外,一旦为应用程序确定了一组成员函数和规则,就有必要对它们进行适当调优,以获得更好的解决方案。这是一项耗时的任务,在一些复杂的系统中可能需要数周到数月的时间。这两种技术都有一些缺点,但两者的结合提供了一个简单的优秀解决方案,并且基于两者的优点,减轻了缺点。此外,该解决方案自动生成代码,以COP800特定的汇编代码或ANSI C语言实现应用程序。联合神经网络模糊逻辑解决方案(NeuFuz):正如在引言中所讨论的,神经网络和kzzy逻辑技术都有许多优点和一些缺点。该组合方案利用神经网络的学习能力,并将其应用于模糊逻辑的隶属函数和规则生成,从而形成一个集成的解决方案,为设计工程师提供了一个巨大的工具。神经网络最显著的优势,是网络学习应用系统行为的能力。一个过程,在这个过程中,工程师应用一些数据点来表示输出,作为控制应用程序中选择的输入的函数。用于训练网络的数据点是设计者了解系统预期功能的结果,并由系统需求、测量或模拟决定。数据点也是系统控制面的部分表示。神经网络具有泛化和确定任何输入值的输出的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural networks and fuzzy logic made simple
Engineers and scientists are in a constant strive to emulate the learning process in the human brain, it's ability to generalize and adapt to changes. The neural network technology was developed with this goal in mind, and is currently being used in real time control applications benefiting from these advanced features. Fuzzy logic on the other hand is a problem solving technique that uses approximate description of the system using simple rules in a human language like English. At first glance, these technologies appear difficult to comprehend and intimidating to use in an actual application. This paper will bring both of these technologies to an understanding level and will show how the combination of both technologies, NeuFuz, takes advantage of both Neural Networks and Fuzzy Logic by eliminating their drawbacks, thus providing the design engineer with a very simple tool that reduces design time and provides a more effective solution. A design of a fan controller will be discussed as an application of this technology. 1.0 Introduction Control applications today demand more and more intelligence and flexibility. These requirements demand for a control system that can handle non linearity and time variation. Moreover the control system should be simple to understand and easy to develop. Conventional techniques are not well suited for nonlinear time variant applications. Neural networks is an elegant solution for nonlinear applications. It requires a number of data sets representing the output as a function of the inputs, covering the entire range of operation. This data is used to train the neural network. Once the training is completed the neural network can generalize and determine an output from any input applied. The neural network is a network of interconnected neurons, where each neuron has the ability to compute an output signal based on all the incoming signals from the neurons connected to it in the previous stage. This output signal is then multiplied by the corresponding weights (the connection strength) before being transmitted to the neuron on the next stage. The neural network weights, or in other words, the connection strengths between neurons are determined in a process called learning, ISBN# 0-7803-2636-9 596 the process of training the neural network for a specific application to a desired level of accuracy. Although this technology can handle non linear system requirements well but is often expensive to implement. Fuzzy Logic is another technique that has been proven very successhl in solving nonlinear problems in many areas where mathematical modeling of the system is difficult or costly to implement. Fuzzy logic offers in these design cases simplicity and ease in design. However Fuzzy logic has some problems as well: The process in Fuzzy Logic is to determine membership functions for the inputs and outputs and generate a number of rules for the application . One important factor here is that membership functions and rules have a substantial effect on the output and therefor on the operation of the control system. When the complexity of the system increases, the set of membership fknctions and rules are difficult to generate. Moreover, once a set of membership functions and rules is determined for an application, it is necessary to properly tune them for a better solution. This is a time consuming task that can take weeks to months in some complex systems. Both of these technologies have some shortcomings, but the combination of the two offers an excellent solution that is made simple, and is based on the advantages of both, alleviating the drawbacks. In addition, this solution automatically generates code that implements the application in either COP800 specific assembly code or in ANSI C language. 2.0 The Combined Neural network Fuzzy Logic Solution (NeuFuz): As discussed in the introduction, both technologies neural networks and kzzy logic have many advantages and some disadvantages. The combined solution takes advantage of the neural networks learning capability and applies it to resolve the membership functions and rules generation of the Fuzzy logic, therefor coming up with an integrated solution that offers a tremendous tool to the design engineer. The most significant advantage of neural networks, is the ability of the net to learn the system behavior of the application. A process in which the engineer applies a number of data points representing the output as a function of the inputs selected in the control application. The data points used to train the net are a result of the designer’s knowledge of the system’s expected fbnctionality and determined by the system requirements, measurements or simulations. The data points are also a partial representation of the control surface of the system. The neural network has the ability to generalize and determine the output for any input value applied. The training process of the neural network is applying the input values to the net and adjusting the weights of the neuron connections between the layers to get the appropriate output value to the required accuracy of the system. The trained network and internal weight distribution can now be mapped into membership fimctions and kzzy rules. As can be seen in this discussion, the neural network advantage is used to resolve the above mentioned problem associated with hzzy logic rules and membership functions. The integration of both of these technologies
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