By Manfred M. Fischer, Yee Leung
Geocomputation should be seen because the software of a computational technology paradigm to review a variety of difficulties in geographical platforms contexts.
This quantity provides a transparent, complete and carefully cutting-edge review of present study, written through top figures within the field.
It presents vital insights into this new and swiftly constructing box and makes an attempt to set up the foundations, and to boost ideas for fixing actual international difficulties in a big selection of program domain names with a catalyst to bigger realizing of what geocomputation is and what it entails.
The huge assurance makes it valuable analyzing for resarchers and execs in geography, environmental and monetary sciences in addition to for graduate scholars of spatial technological know-how and machine science.
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3 Stop? Major steps ofEPNet The major steps of EPNet are deseribed by Fig. 3, whieh are explained further as follows (Yao and Liu 1997, 1998b): (i) (ii) (iii) (iv) Generate an initial population of M networks at random. The number of hidden nodes and the initial eonneetion density for eaeh network are uniformly generated at random within eertain ranges. The random initial weights are uniformly distributed inside a small range. Partially train eaeh network in the population on the training set for a eertain number of epoehs using a modified BP [MBP] with adaptive leaming rates.
In its current implementation, EPNet is used to evolve feedforward CNNs with sigmoid transfer functions. However, this is not an inherent constraint. In fact, EPNet has minimal constraint on the type ofCNNs which may be evolved. The feedforward CNNs do not have to be strictly layered or fully connected between adjacent layers. They mayaiso contain hidden nodes with different transfer functions (Liu and Yao 1996a). 3 Stop? Major steps ofEPNet The major steps of EPNet are deseribed by Fig. 3, whieh are explained further as follows (Yao and Liu 1997, 1998b): (i) (ii) (iii) (iv) Generate an initial population of M networks at random.
The first m circ1es are really just copies of the inputs XI. , X m. Every other node in the network, such as node number i, which calculates neti and Xi, takes inputs from every node that precedes it in the network. Even the last output node [the (m+N n)th], which generates Ym takes input from other output nodes, such as the one which outputs Yn- I • The direct encoding scheme is used in EPNet to represent CNN architectures and connection weights [inc1uding biases]. This is necessary because EPNet evolves CNN architectures and weights simultaneously and needs information about every connection in an CNN.
