Multi-strategy learning and deep harmony memory improvisation for self-organizing neurons

This study proposes a concept of representation learning by implementing multi-strategy deep learning harmony memory improvisation for selecting the best harmony of self-organizing neurons. Representation learning is a set of methods that allows a machine to be fed with raw data and to automatically discover the representations needed for detection or classification. In our study, the deep multi-strategy learning involves the convolution of the self-organizing neurons with deep harmony memories improvisation in self-organizing and representation of map learning. The convolution of self-organizing neurons and harmony memory optimize the representation neurons’ weights by generating the optimal best matching unit which is represented as fitness function of \(f_1 \left( x \right) \) and \(f_2 \left( x \right) \). While \(f_1 \left[ {g\left( {{f}''_1 \left( x \right) } \right) } \right] \) and \(f_2 \left[ {g\left( {{f}'_2 \left( x \right) } \right) } \right] \) represent the New Harmony fitness function. The best fitness function, \(f_{{ best}} (x)\) is selected based on the \(f_1 \left( x \right) \) and \(f_2 \left( x \right) \) performance which will be later stored in the harmony memory vector. The position vector of a particle is subjected to the Newtonian mechanics constant acceleration during the interval \(\Delta t\). The search space of self-organizing map with Newton-based particle swarm algorithm particles depends on the width area, \(\sigma _\alpha (t)\) of organizing neurons lattice structure. Our proposed methods are experimented on various biomedical datasets. The findings indicate that the proposed methods provide better quantization error for clustering and good classification accuracy with statistical measurement validations.