2. Should I use a conventional or"fuzzy processor"for implementation? We have typically found that our needs can be met if we use a conventional processor that has a better track record with reliability; however, there may be some advantages to fuzzy processors when large rule-bases are used and fast sampling times are needed 3. Are there special"tricks of the trade"in the implementation of fuzzy controllers that have many rules? Yes 4. Does fuzzy control provide for a user-friendly way to tune the controller during implementation studies? Often it does. We have found in field studies that when you know generally what to do to get a controller to work, it is sometimes hard to get this information into the gains of a conventional controller and easier to express it in rules and load them into a fuzzy controller or fuzzy supervisor Overall, in comparing fuzzy to conventional control, it is interesting to note that there are conventional control schemes that are analogous to fuzzy ones:(1) direct fuzzy control is analogous to direct nonlinear control,(2)fuzzy adaptive control is analogous to conventional adaptive control (e. g, model reference adaptive control), and(3) fuzzy supervisory control is analogous to hierarchical control. Does there exist an analogous conventional approach to every fuzzy control scheme? If so, then in doing fuzzy control research it seems to be very important to compare and contrast he performance of the fuzzy versus the conventional approaches 4.3 Neural Networks Artificial neural networks are circuits, computer algorithms, or mathematical representations of the massively connected set of neurons that form biological neural networks. They have been shown to be useful as an alternative computin technology and have proven useful in a variety of pattern recognition, signal processing, estimation, and control problems. Their capabilities to learn from examples have been particularly usefi In this section we will introduce two of the more popular neural networks and discuss how they relate to the areas of uzzy systems and control. We must emphasize that there are many topics in the area of neural networks that are not covered here. For instance, we do not discuss associative memories and Hopfield neural networks, recurrent networks, Boltz-mann machines, or Hebbian or competitive learning. We refer the reader to Section 4.8, For Further Study, for references that cover these topics in detai 4.3.1 Multilayer Perceptrons The multilayer perceptron is a feed-forward neural network (i.e, it does not use past values of its outputs or other internal variables to compute its current output ). It is composed of an interconnection of basic neuron processing units The neuron For a single neuron, suppose that we use, i=1, 2,,n, to denote its inputs and suppose that it has a single output y Figure 4.1 shows the neuron. Such a neuron first forms a weighted sum of the inputs wherea; are the interconnection"weights"andes the"bias"for the neuron( these parameters model the interconnections between the cell bodies in the neurons of a biological neural network). The signal represents a signal in the biological neuron, and the processing that the neuron performs on this signal is represented with an"activation function. " Thi activation function is represented with a function f, and the output that it computes is PDF文件使用" pdffactory Pro"试用版本创建ww. fineprint,com,cn2. Should I use a conventional or "fuzzy processor" for implementation? We have typically found that our needs can be met if we use a conventional processor that has a better track record with reliability; however, there may be some advantages to fuzzy processors when large rule-bases are used and fast sampling times are needed. 3. Are there special "tricks of the trade" in the implementation of fuzzy controllers that have many rules? Yes. 4. Does fuzzy control provide for a user-friendly way to tune the controller during implementation studies? Often it does. We have found in field studies that when you know generally what to do to get a controller to work, it is sometimes hard to get this information into the gains of a conventional controller and easier to express it in rules and load them into a fuzzy controller or fuzzy supervisor. Overall, in comparing fuzzy to conventional control, it is interesting to note that there are conventional control schemes that are analogous to fuzzy ones: (1) direct fuzzy control is analogous to direct nonlinear control, (2) fuzzy adaptive control is analogous to conventional adaptive control (e.g., model reference adaptive control), and (3) fuzzy supervisory control is analogous to hierarchical control. Does there exist an analogous conventional approach to every fuzzy control scheme? If so, then in doing fuzzy control research it seems to be very important to compare and contrast the performance of the fuzzy versus the conventional approaches. 4.3 Neural Networks Artificial neural networks are circuits, computer algorithms, or mathematical representations of the massively connected set of neurons that form biological neural networks. They have been shown to be useful as an alternative computing technology and have proven useful in a variety of pattern recognition, signal processing, estimation, and control problems. Their capabilities to learn from examples have been particularly useful. In this section we will introduce two of the more popular neural networks and discuss how they relate to the areas of fuzzy systems and control. We must emphasize that there are many topics in the area of neural networks that are not covered here. For instance, we do not discuss associative memories and Hopfield neural networks, recurrent networks, Boltz-mann machines, or Hebbian or competitive learning. We refer the reader to Section 4.8, For Further Study, for references that cover these topics in detail. 4.3.1 Multilayer Perceptrons The multilayer perceptron is a feed-forward neural network (i.e., it does not use past values of its outputs or other internal variables to compute its current output). It is composed of an interconnection of basic neuron processing units. The Neuron For a single neuron, suppose that we use , i = 1,2,..., n, to denote its inputs and suppose that it has a single output y. Figure 4.1 shows the neuron. Such a neuron first forms a weighted sum of the inputs 1 n i i i z x w q = æ ö = - ç ÷ è ø å whereωi are the interconnection "weights" andθis the "bias" for the neuron (these parameters model the interconnections between the cell bodies in the neurons of a biological neural network). The signal z represents a signal in the biological neuron, and the processing that the neuron performs on this signal is represented with an "activation function." This activation function is represented with a function f, and the output that it computes is PDF 文件使用 "pdfFactory Pro" 试用版本创建 www.fineprint.com.cn