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Although the echo custom server has now been built successfully, you still need to carry out some more steps to get any response from it. First we will install it, and in the subsequent sections we will actually load it and communicate with it. In order for a custom server to be loadable by ectcpadapter, it must be located in the \system\libs directory. The custom servers that are shipped with a Symbian OS phone are located on the z:\system\libs directory on the ROM, but that is no good to somebody developing add-ons. Luckily, it is also possible to load a custom server from c:\system\libs as well. If you are running the emulator then building it will automatically put the binary in the right directory on the notional z: drive. If you are building a ROM for a reference board then you can add your binary to the ROM yourself. If you want to try your custom server out on a real Symbian OS phone then you have two ways to get it onto the device: 1. Use a le browser to copy the custom server directly. This is the easiest method for Connectivity developers. 2. Create a .sis installer le and then use the application installer.

Computational Intelligence: An Introduction, Second Edition AP Engelbrecht c 2007 John Wiley & Sons, Ltd

Stellar Polarimetry David Clarke Copyright 2010 WILEY-VCH Verlag GmbH & Co KGaA, Weinheim ISBN: 978-3-527-40895-5

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another debate! However, the contents of this book may help to shed some light on the answer to this question A more recent de nition of arti cial intelligence came from the IEEE Neural Networks Council of 1996: the study of how to make computers do things at which people are doing better A de nition that is awed, but this is left to the reader to explore in one of the assignments at the end of this chapter This book concentrates on a sub-branch of AI, namely Computational Intelligence (CI) the study of adaptive mechanisms to enable or facilitate intelligent behavior in complex and changing environments These mechanisms include those AI paradigms that exhibit an ability to learn or adapt to new situations, to generalize, abstract, discover and associate The following CI paradigms are covered: arti cial neural networks, evolutionary computation, swarm intelligence, arti cial immune systems, and fuzzy systems While individual techniques from these CI paradigms have been applied successfully to solve real-world problems, the current trend is to develop hybrids of paradigms, since no one paradigm is superior to the others in all situations In doing so, we capitalize on the respective strengths of the components of the hybrid CI system, and eliminate weaknesses of individual components The rest of this chapter is organized as follows: Section 11 of this chapter presents a short overview of the di erent CI paradigms, also discussing the biological motivation for each paradigm A short history of AI is presented in Section 12 At this point it is necessary to state that there are di erent de nitions of what constitutes CI This book re ects the opinion of the author, and may well cause some debate For example, swarm intelligence (SI) and arti cial immune systems (AIS) are classi ed as CI paradigms, while many researchers consider these paradigms to belong only under Arti cial Life However, both particle swarm optimization (PSO) and ant colony optimization (ACO), as treated under SI, satisfy the de nition of CI given above, and are therefore included in this book as being CI techniques The same applies to AISs.

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The le browser option is quick and easy, and if you are writing Connectivity software then you have an advantage in that you may well already have a le browser available. However, you cannot expect an end-user to copy the binary manually. Using the le browser is perfectly ne during development, but eventually you will need to create a proper installer as described in 6.

Various multilayer NN types have been developed Feedforward NNs such as the standard multilayer NN, functional link NN and product unit NN receive external signals and simply propagate these signals through all the layers to obtain the result (output) of the NN There are no feedback connections to previous layers Recurrent NNs, on the other hand, have such feedback connections to model the temporal characteristics of the problem being learned Time-delay NNs, on the other hand, memorize a window of previously observed patterns

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