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This book considers ve main paradigms of Computation Intelligence (CI), namely arti cial neural networks (NN), evolutionary computation (EC), swarm intelligence (SI), arti cial immune systems (AIS), and fuzzy systems (FS) Figure 11 gives a summary of the aim of the book In addition to CI paradigms, probabilistic methods are frequently used together with CI techniques, which is also shown in the gure Soft computing, a term coined by Lot Zadeh, is a di erent grouping of paradigms, which usually refers to the collective set of CI paradigms and probabilistic methods The arrows indicate that techniques from di erent paradigms can be combined to form hybrid systems Each of the CI paradigms has its origins in biological systems NNs model biological

Figure 11 Computational Intelligence Paradigms neural systems, EC models natural evolution (including genetic and behavioral evolution), SI models the social behavior of organisms living in swarms or colonies, AIS models the human immune system, and FS originated from studies of how organisms interact with their environment

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The brain is a complex, nonlinear and parallel computer It has the ability to perform tasks such as pattern recognition, perception and motor control much faster than any computer even though events occur in the nanosecond range for silicon gates, and milliseconds for neural systems In addition to these characteristics, others such as the ability to learn, memorize and still generalize, prompted research in algorithmic modeling of biological neural systems referred to as artificial neural networks (NN) It is estimated that there is in the order of 10-500 billion neurons in the human cortex, with 60 trillion synapses The neurons are arranged in approximately 1000 main modules, each having about 500 neural networks Will it then be possible to truly model the human brain Not now Current successes in neural modeling are for small arti cial NNs aimed at solving a speci c task Problems with a single objective can be solved quite easily with moderate-sized NNs as constrained by the capabilities of modern computing power and storage space The brain has, however, the ability to solve several problems simultaneously using distributed parts of the brain We still

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The basic building blocks of biological neural systems are nerve cells, referred to as neurons As illustrated in Figure 12, a neuron consists of a cell body, dendrites and an axon Neurons are massively interconnected, where an interconnection is between the axon of one neuron and a dendrite of another neuron This connection is referred to as a synapse Signals propagate from the dendrites, through the cell body to the axon; from where the signals are propagated to all connected dendrites A signal is transmitted to the axon of a neuron only when the cell res A neuron can either inhibit or excite a signal

Figure 12 A Biological Neuron An arti cial neuron (AN) is a model of a biological neuron (BN) Each AN receives signals from the environment, or other ANs, gathers these signals, and when red, transmits a signal to all connected ANs Figure 13 is a representation of an arti cial neuron Input signals are inhibited or excited through negative and positive numerical weights associated with each connection to the AN The ring of an AN and the strength of the exiting signal are controlled via a function, referred to as the activation function The AN collects all incoming signals, and computes a net input signal as a function of the respective weights The net input signal serves as input to the activation function which calculates the output signal of the AN

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An arti cial neural network (NN) is a layered network of ANs An NN may consist of an input layer, hidden layers and an output layer ANs in one layer are connected, fully or partially, to the ANs in the next layer Feedback connections to previous layers are also possible A typical NN structure is depicted in Figure 14

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