Finite-time stability analysis for neutral-type neural networks with hybrid time-varying delays without using Lyapunov method

Basic theorem and global exponential stability of differential-algebraic neural networks with delay

A differential-algebraic neural network (DANN) with delay (DDANN) is proposed. Firstly, the global existence and uniqueness theorems are established for a DDANN, respectively. Next, a new differential-algebraic inequality is established. Then, a theorem on global exponential stability of DDANN is shown by using this inequality. As an application of DDANN, a very concise criterion on global exponential stability for a neutral-type neural network is given by using DDANNs. Finally, two examples are given to illustrate the theoretical results.

A new Lyapunov functional for stability analysis of neutral-type Hopfield neural networks with multiple delays

This research paper conducts an investigation into the stability issue for a more general class of neutral-type Hopfield neural networks that involves multiple time delays in the states of neurons and multiple neutral delays in the time derivatives of the states of neurons. By constructing a new proper Lyapunov functional, an alternative easily verifiable algebraic criterion for global asymptotic stability of this type of Hopfield neural systems is derived. This new stability condition is entirely independent of time and neutral delays. Two instructive examples are employed to indicate that the result obtained in this paper reveals a new set of sufficient stability criteria when it is compared with the previously reported stability results. Therefore, the proposed stability result enlarges the application domain of Hopfield neural systems of neutral types.

New Criteria for Stability of Neutral-Type Neural Networks With Multiple Time Delays

This research work studies stability problems for more general models of neutral-type neural systems where both neuron states and the time derivative of neuron states involve multiple delays. Some new sufficient criterion is presented, which guarantee the existence, uniqueness, and global asymptotic stability of equilibrium points of the considered neural network model. These obtained stability conditions, which can be applied to some larger classes of general neural network models, are based on the analysis of a new and improved suitable Lyapunov functional. The proposed conditions are independent of time delay parameters and can be easily justified by examining some certain relationships among the relevant neural network parameters. This paper also shows that the obtained stability criteria can be considered as the generalization of some previously reported corresponding stability conditions for neural networks, including multiple time delay parameters.

PERSON-Personalized Expert Recommendation System for Optimized Nutrition

The rise of personalized diets is due to the emergence of nutrigenetics and genetic tests services. However, the recommendation system is far from mature to provide personalized food suggestion to consumers for daily usage. The main barrier of connecting genetic information to personalized diets is the complexity of data and the scalability of the applied systems. Aiming to cross such barriers and provide direct applications, a personalized expert recommendation system for optimized nutrition is introduced in this paper, which performs direct to consumer personalized grocery product filtering and recommendation. Deep learning neural network model is applied to achieve automatic product categorization. The ability of scaling with unknown new data is achieved through the generalized representation of word embedding. Furthermore, the categorized products are filtered with a model based on individual genetic data with associated phenotypic information and a case study with databases from three different sources is carried out to confirm the system.