Fly visual evolutionary neural network solving large‐scale global optimization

Abstract

Neurophysiologic achievements claimed that the fly visual system could naturally contribute to a type of artificial computation model which used motion‐sensitive neurons to detect the local movement direction changes of moving objects. It, however, still remains open how the neurons' information‐processing mechanisms and the inspirations of swarm intelligence can be integrated to serve an interdisciplinary topic between computer vision and intelligence optimization‐visual evolutionary neural networks. Hereby, a fly visual evolutionary neural network is developed to solve large‐scale global optimization (LSGO), inspired by swarm evolution and the characteristics of fly visual perception. It includes two functional modules, of which one is to generate global and local motion direction activities of visual neural nodes, and the other takes the activities as learning rates to update the nodes' states by a population‐like evolutionary strategy. Also, it is used to optimize the structure of a multilayer perceptron to acquire a sample classification model. The theoretical results indicate that the network is convergent and meanwhile the computational complexity mainly depends on the size of the input layer and the dimension of LSGO. The comparative experiments have verified that the network is an extremely competitive optimizer for LSGO problems.

An improved stochastic gradient descent algorithm based on Rényi differential privacy

“…Second, the deep neural network (DNN) has a large number of hidden layers, which are enough to encode the characteristics of some individual data into model parameters, and even to remember characteristic parameters of the entire data set. 4,5,6 CHENG ET AL.…”

“…After data is collected, users often have no control over how the model uses or shares their information. Second, the deep neural network (DNN) has a large number of hidden layers, which are enough to encode the characteristics of some individual data into model parameters, and even to remember characteristic parameters of the entire data set 4,5,6 . Fredrikson et al 7 implemented a model inversion attack to recover images from facial recognition systems.…”

A neural autonomous robotic manipulator with three degrees of freedom

“…Due to their flexibility, ANNs can be used in different applications, such as universal function approximator, process control and robotics, 26,27 pattern classifier, [28][29][30] time series prediction, function optimization, computer vision, 31 large-scale global optimization, 32 image authenticity verification, 33 time-varying quadratic programming, 34 and image improvement and restoration. 35 Other alternative techniques for image improvement and reconstruction have also stood out in the scientific community, as can be seen in the works [36][37][38].…”