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N-Body Potential Interaction as a Cost Function in the Elastic Model for SANET Cloud Computing

Chaczko Z., Resconi G., Chiu C., Aslanzadeh S.

International Journal of Electronics and Telecommunications

2012

Vol, 58, No. 1

63-70

Given a connection graph of entities that send and receive a flow of data controlled by effort and given the parameters, the metric tensor is computed that is in the elastic relational flow to effort. The metric tensor can be represented by the Hessian of the interaction potential. Now the interaction potential or cost function can be among two entities: 3 entities or 'N' entities and can be separated into two main parts. The first part is the repulsion potential the entities move further from the others to obtain minimum cost, the second part is the attraction potential for which the entities move near to others to obtain the minimum cost. For Pauli's model [1], the attraction potential is a functional set of parameters given from the environment (all the elements that have an influence in the module can be the attraction of one entity to another). Now the cost function can be created in a space of macro-variables or macro-states that is less of all possible variables. Any macro-variable collect a set of micro-variables or microstates. Now from the hessian of the macro-variables, the Hessian is computed of the micro-variables in the singular points as stable or unstable only by matrix calculus without any analytical computation - possible when the macro-states are distant among entities. Trivially, the same method can be obtained by a general definition of the macro-variable or macro-states and micro-states or variables. As cloud computing for Sensor-Actor Networks (SANETS) is based on the bonding concept for complex interrelated systems; the bond valence or couple corresponds to the minimum of the interaction potential V and in the SANET cloud as the minimum cost.

Reasoning by SVD and Morphotronic Network

Chaczko Z., Resconi G.

International Journal of Electronics and Telecommunications

2011

Vol. 57, No. 2

145-152

The immune system of the vertebrates possess the capabilities of “intelligent” information processing, which include memory, the ability to learn, to recognize, and to make decisions with respect to unknown situations. The mathematical formalization of these capabilities forms the basis of immune-computing (IC) as a new computing approach that replicates the principles of information processing by proteins and immune networks. This IC approach looks rather constructive as a basis for a new kind of computing. With the Morphotronic System or the analogous SVD we can create effective learning process and create immune memory by the projection operators. Given the immune memory is possible to recognize and compare antigen in a way to take defense action to eliminate the dangerous cell.