According to random projection theory, we will implement random projection with random matrices rather than fixed matrices as applied in [9,10].
The projection of populations growth is implemented through projection development of built-up area which adapted by the landscape to be a sustainability landscape.
This constraint is implemented via projection within the algorithm, where the projected solution x′ is calculated as Eq. (7).
In next version, we are planning to implement the projection of profiles onto orthonormal bases, which should improve our performances.
SOM implements nonlinear projection of multidimensional data onto a two-dimensional array of weight vectors, and this effectively preserves the topology of the high-dimensional data space [ 8– 10].
In 1999, Solodov and Svaiter [3] proposed an algorithm for solving the VI ( C, f ) in an Euclidean space, known as the double projection algorithm due to the fact that one needs to implement two projections in each iteration.
Here we propose a web-based interface to the DT-Hybrid algorithm, which applies a recommendation technique based on bipartite network projection implementing resources transfer within the network.
3. Instrumentation code, e.g. for attaching signal sources (current and/or voltage clamps, synapses used to implement afferent projections), setting up Vector record and/or stimulation, recording of spike times, extracellular recording, file i/o, etc.. 4. Simulation flow control code.
Two main approaches to implement random projections in a SN are discussed in the literature [163].
In Algorithms 3.1 and 3.2, there still exists difficulty to implement the projections P C and P Q with respect to the defined norms, especially when C and Q are general closed convex sets.
