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vb.net barcode reader free STATISTICS in Software
STATISTICS Code 128 Code Set A Scanner In None Using Barcode Control SDK for Software Control to generate, create, read, scan barcode image in Software applications. Making Code 128 In None Using Barcode creation for Software Control to generate, create Code128 image in Software applications. Statistics is the branch of mathematics that is concerned with possibilities. Statistical methods can assign a value to how likely something is. For example, given a halfdozen distance sensors around your robot, you should be able to calculate the probability that you are in a corner, or between two walls. Bayesian analysis is particularly relevant to the patternmatching problem. In daily life, you are most likely to find Bayesian techniques in your email SPAM filters. Statistics, however, is a complex and arcane field that we cannot do justice to here. Decoding Code128 In None Using Barcode decoder for Software Control to read, scan read, scan image in Software applications. Code 128B Creation In C# Using Barcode encoder for .NET framework Control to generate, create Code 128 Code Set C image in .NET framework applications. CHAPTER 17 Intelligent Behavior FUZZY LOGIC
USS Code 128 Generator In VS .NET Using Barcode encoder for ASP.NET Control to generate, create Code 128 image in ASP.NET applications. Draw Code 128 In .NET Using Barcode drawer for Visual Studio .NET Control to generate, create Code 128 image in Visual Studio .NET applications. Fuzzy logic is the insecure cousin of Boolean logic. Boolean thinking is crisp and precise, definite. The temperature is hot or the temperature is cold. Fuzzy logic allows the temperature to be mostly hot but still a little bit cold. Boolean logic can be seen as a graph like Fig. 177. The moment the temperature passes the 80 degree mark it is suddenly 100% hot. Pow! Figure 178 shows the same thing from a fuzzy perspective. The degree of hotness gradually increases as the temperature goes from 70 to 90 degrees. At 80 degrees, it is about half hot and half cold. Warm, if you will. If you are from any of the Northern states, substitute 70 (or even 60) degrees for our 80 degree Texas definition of warm. When you think in Boolean logic, everything switches 100%. If the temperature is Hot, turn on the fan. On or off. This works well for some environments and it is simple to implement. In fuzzy logic you think in a sliding scale. To the degree that the temperature is Hot, turn on the fan. If the temperature is only a little bit hot, say 75 degrees, the fan turns on low. If the temperature is 100 degrees the fan is spinning at full tilt. This varying output value, from not hot to fully hot, is based on the input s membership in the category hot. There are a number of membership functions, such as those shown in Fig. 179. Printing ANSI/AIM Code 128 In VB.NET Using Barcode drawer for VS .NET Control to generate, create Code 128A image in .NET framework applications. Code 128 Code Set C Encoder In None Using Barcode drawer for Software Control to generate, create Code 128B image in Software applications. Fig. 177. Data Matrix ECC200 Drawer In None Using Barcode encoder for Software Control to generate, create Data Matrix 2d barcode image in Software applications. UCC  12 Printer In None Using Barcode creation for Software Control to generate, create GTIN  12 image in Software applications. Boolean hot or cold.
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Printing EAN 13 In ObjectiveC Using Barcode generation for iPad Control to generate, create GS1  13 image in iPad applications. GTIN  12 Generator In VB.NET Using Barcode generation for .NET Control to generate, create UPCA image in .NET framework applications. Fig. 179. Paint EAN13 In VS .NET Using Barcode maker for ASP.NET Control to generate, create EAN13 image in ASP.NET applications. Reading Code 128 Code Set A In C# Using Barcode recognizer for .NET framework Control to read, scan read, scan image in VS .NET applications. Membership functions.
Fig. 1710. Fuzzy operators.
There are fuzzy versions of the Boolean operators, too. Instead of a truth table, we represent them as equations and pictures (Fig. 1710). The min( ) function takes the lower of both values, while max( ) takes the larger. Fuzzy logic also has some modifiers that affect the shape of the membership function. If you want to know if something is very hot and not just hot, you could shape the Hot membership function using the Very modifier before you check the temperature against it. Some modifiers, also known as hedges, are shown in Fig. 1711. These assume that the output value for 100% membership in a category is 1.00. The hedges take the membership function s points and raise them to a power. Powers greater than 1.0 tighten the function, making it harder to be a full CHAPTER 17 Intelligent Behavior
Fig. 1711. Fuzzy hedges.
member of the category. Lower powers bloat the membership function, making it easier to qualify. Fuzzy logic is a simple form of pattern matching. It classifies its input into membership categories. These membership values can then be used to weight the actions associated with that category. If several categories are trying to drive the same output, their conflicting control values can be averaged together, using the priorities defined by their membership weights. NEURAL NETWORKS
Computational neurons, and the collections of neurons known as neural networks, are patternmatching modules based on biological neurons. Each neuron takes one or more inputs that it compares against its inner template. The output of the neuron is then active to the extent that the input matches that template. Figure 1712 shows a diagram of a typical computational neuron, known as a McCullochPitts neuron. Though a bit scarylooking, its operation is simple. The input is a vector, or list, of numbers called x, each entry of which is indexed as x(i). Internal to the neuron is a set of weights w, one for each input. The sigma operator is a loop that adds up the results from its equation, which is calculated once for every input index. If there are three inputs, the equivalent equation is: y w 1 x 1 w 2 x 2 w 3 x 3 176 y is the output value. This function is called a dot product, and it is one method of calculating how closely two vectors match. It also has further mathematical significance, not relevant to this discussion.

