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Both neural networks and fuzzy logic are universal estimators; they can approximate any nonlinear function to any prescribed accuracy, provided that suf cient hidden neurons or fuzzy rules are available. Resent results show that the fusion procedure of these two different technologies seems to be very effective for nonlinear system identi cation [7]. Over the past few years, the application of fuzzy neural networks to nonlinear system identi cation has been a very active area [28]. Fuzzy modeling involves structure and parameters identi cation. Neural networks can be classi ed as feedforward and recurrent [15]. Feedforward networks, for example, multilayer perceptrons (MLPs), are implemented for the approximation of nonlinear functions. The main drawbacks of these neural networks are that the updating of the weights can fall in a local minimum, and the function approximation is sensitive to the training data [30]. Since recurrent networks incorporate feedback, they have powerful representation capability and can successfully overcome disadvantages of feedforward networks [26]. System identi cation can be classi ed into two groups: (1) of ine identi cation [4, 9, 18, 32, 38, 41]; (2) online identi cation [2, 3, 20, 21, 23, 24, 27, 31, 39, 40, 42]. In of ine identi cation, the update of the parameters and the structure takes place only after the whole training data set has been presented, that is, only after each epoch. Structure identi cation is used to select fuzzy rules. It often relies on a substantial amount of heuristic observation to express proper knowledge of a strategy. It is often tackled by of ine, trial-and-error approaches, like the unbiasedness criterion [32]. Several approaches generate fuzzy rules from numerical data. One of the most common methods for structure initialization is uniform partitioning of each input variable into fuzzy sets, resulting in a fuzzy grid. This approach is followed in ANFIS [18]. In [4], the TakagiSugeno model was used for designing various neuro-fuzzy systems. This approach consists of two learning phases: (1) structure learning, which involves nding the main input variables out of all the possible ones specifying the membership functions, the partitioning of the input space, and determining the number of fuzzy rules; and (2) parameter learning, which involves the determination of the unknown parameters and the optimization of the existing ones using some mathematical optimization method. The latter phase is based on linguistic information from the human expert and on the numeric data obtained from the actual system to be modeled. The parameter updating is employed after the structure is decided. Most structure identi cation methods are based on data clustering, such as fuzzy C-means clustering [5], mountain clustering [41], and subtractive clustering [9]. In online identi cation, model structure and parameter identi cation are updated immediately after each input output pair has been presented, that is, after each iteration. Online identi cation also includes (1) model structure and (2) parameter identi cation. For structure identi cation, mainly clustering methods are used. In clustering, to update fuzzy rules, distance from centers of fuzzy rules, potentials of new data sample, or errors from previous steps have been used. Different mechanisms are used in constructing the structure. The resource allocating network (RAN) [31] uses a geometric growing
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10.6 TOPOLOGY CONSTRUCTION IN HETEROGENEOUS NETWORKS The homogeneity assumption usually does not hold in practice for various reasons. First, even devices of the same type may have slightly different maximal transmission power. Second, the environment settings also affect the transmission range. For instance, a wireless device usually has a larger transmission range with fewer obstacles around it. Finally, as mentioned in Section 10.2, there exist devices of dramatically different capabilities in wireless sensor networks, for example, lightweight sensor nodes versus energy-rich cluster heads. In this section, we consider the issue of topology construction in heterogeneous wireless networks, where the transmission range of each node may be different. Most of the topology construction algorithms discussed in Section 10.5 assume homogeneous wireless nodes with uniform transmission ranges. When directly
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For R < RB , n is less than 2, and for R > RB , n approaches 4. It was found that on non-LOS paths in an urban environment with low base station antennas and with users at street level, propagation takes place down streets and around corners rather than over buildings. For these non-LOS paths the signal must turn corners by multiple re ections and diffraction at vertical edges of buildings. Field tests reveal that signal level decreases by about 20 dB when turning a corner. In the case of propagation inside buildings where the transmitter and receiver are on the same oor, the key factor is the clearance height between the average tops of furniture and the ceiling.
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The shifts in society and business over the past decade from hoarding to sharing, from fortress to ecosystem, from spread-out and easily hidden to hyperconnected and hypertransparent combine to put new emphasis on the HOWs of human behavior, the way we ll the synapses between us and others. In this second part, we begin our journey to understand and command these HOWs and to put them to work in everything we do. The rst step leads us inward, to the thought processes and understandings that shape our decisions and actions toward others. A little biology, a little sociology, a little linguistics, and a little golf: This section is called How We Think, and in it, we re going to begin to build a new framework for understanding a world of HOW.
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There are two security modes in the IEEE 802.15.3 HR-WPAN piconet: modes 0 and 1. These indicate whether or not security is used in a current piconet. If a DEV is operating in mode 0, this means that the MAC frames do not contain any type of cryptographic security. If a DEV is operating in mode 0 and receives a frame with the mode eld set to 1, then the frame is discarded. When the SEC eld for a superframe is set to 1, a mechanism is used that provides use of symmetric-key cryptography. This security service protects frames by using encryption and frame integrity checks. If a PNC or DEV
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