neural network

The relevance of creating a comprehensive system for meat control and identification to determine its freshness level has been demonstrated in the study. The drawbacks of traditional organoleptic and laboratory methods commonly used for meat inspection were analyzed. The authors presented the advantages and challenges of employing an electronic nose. A design for a meat control and identification system was proposed, which includes an Arduino Uno microcontroller, Raspberry Pi, USB to TTL adapter, gas sensors, color sensor, thermal camera, and image sensor.

This paper investigates the problem of embedding features used in datasets for training neural networks. The use of embeddings increases the performance of neural networks, and therefore is an important part of data preparation for deep learning methods. Such a process is based on semantic metrics. It is proposed to use ontologies of the subject areas to which the corresponding feature belongs for embedding. This work developed such a method and investigated its use for the task of categorizing text documents. The research results showed the advantage of the developed method.

It is shown that the following approaches can be used for group management of mobile robotic platforms (MRP): centralized (concentrated), decentralized (distributed) and hybrid. It was determined that an urgent task is the development of a neurofuzzy management system for the MRP group, which must perform the distribution of tasks between the MRPs, the determination of MRP movement routes, joint planning of works and their synchronization.

In this paper, we construct a new conjugate gradient method for solving unconstrained optimization problems.  The proposed method satisfies the sufficient decent property irrespective of the line search and the global convergence was established under some suitable.  Further, the new method was used to train different sets of data via a feed forward neural network.  Results obtained show that the proposed algorithm significantly reduces the computational time by speeding up the directional minimization with a faster convergence rate.

The requirements for a mobile robotic platform (MRP) with an intelligent traffic control system and data transmission protection are determined. Main requirements are the reduction of dimensions, energy consumption, and cost; remote and intelligent autonomous traffic control; real-time cryptographic data protection; preservation of working capacity in the conditions of action of external factors; adaptation to customer requirements; ability to perform tasks independently in conditions of uncertainty of the external environment.

 Features of designing a system of automated generation of poetic works, which opens up new opportunities for artistic speech and show business, especially the preparation of poems and songs have been considered. Quite often lyrics without special content become successful due to the lack of complex plots, as well as due to the unobtrusiveness and ease of perception by listeners. Well-known literature sources and available software products that can generate poetic works by combining different methods and algorithms are analyzed.

The requirements are formed, the method is chosen and the main stages of development of mobile means of neuro-like cryptographic encryption and real-time data decryption are considered. It is shown that the development of mobile means of neuro-like cryptographic encryption and decryption of real-time data with high efficiency of equipment is reduced to minimize hardware costs while providing a variety of requirements, characteristics and limitations. The tabular-algorithmic method of calculating the scalar product has been improved.

In this paper, we propose a method for optimizing the structure of a multilayer neural network based on minimizing nonlinear generalized error, which is based on the principle of minimum length of description. According to this principle, the generalized error is determined by the error in the description of the model and the error in the approximation of the data by the neural network in the nonlinear approximation.

A model of parallel sorting neural network of discrete-time has been proposed. The model is described by system of difference equations and by step functions. The model is based on simplified neural circuit of discrete-time that identifies maximal/minimal values of input data and is described by difference equation and by step functions. A bound from above on a number of iterations required for reaching convergence of search process to steady state is determined. The model does not need a knowledge of change range of input data.

In the article were researched the principles of building systems for observation and recognition of objects. Also we have given the classification of human faces recognition methods. Authors have analized the features of operetion for the progressive calibration network (PCN) for human face recognition. And finally has been created and tested the developed face recognition algorithm as the realized software system.