Inventory management is complex nonlinear systems that are affected by various external factors, including course human action and policy. We study the inventory management model under special circumstances and analyse the equilibrium point of the system. The dynamics of the system is analysed by means of the eigenvalue trajectory, bifurcations, chaotic attractor, and largest Lyapunov exponent diagram. At the same time, according to the definition of fractional calculus, the fractional approximate entropy is used to analyse the system, and the results are consistent with those of the largest Lyapunov exponent diagram, which shows the effectiveness of this method.

Precise inventory prediction is the key to goods inventory and safety management. Accurate inventory prediction improves enterprises’ production efficiency. It is also essential to control costs and optimize the supply chain’s performance. Nevertheless, the complex inventory data are often chaotic and nonlinear; high data complexity raises the accuracy prediction difficulty. This study simulated inventory records by using the dynamics inventory management system. Four deep neural network models trained the data: short-term memory neural network (LSTM), convolutional neural network-long short-term memory (CNN-LSTM), bidirectional long short-term memory neural network (Bi-LSTM), and deep long-short-term memory neural network (DLSTM). Evaluating the models’ performance based on RMSE, MSE, and MAE, bi-LSTM achieved the highest prediction accuracy with the least square error of 0.14%. The results concluded that the complexity of the model was not directly related to the prediction performance. By contrasting several methods of chaotic nonlinear inventory data and neural network dynamics prediction, this study contributed to the academia. The research results provided useful advice for companies’ planned production and inventory officers when they plan for product inventory and minimize the risk of mishaps brought on by excess inventories in warehouses.

In this paper, a class of fractional-order symmetric hyperchaotic systems is studied based on the Adomian decomposition method. Starting from the definition of Adomian, the nonlinear term of a fractional-order five-dimensional chaotic system is decomposed. At the same time, the dynamic behavior of a fractional-order hyperchaotic system is analyzed by using bifurcation diagrams, Lyapunov exponent spectrum, complexity and attractor phase diagrams. The simulation results show that with the decrease of fractional order q, the complexity of the hyperchaotic system increases. Finally, based on the fractional-order circuit design principle, a circuit diagram of the system is designed, and the circuit is simulated by Multisim. The results are consistent with the numerical simulation results, which show that the system can be realized, which provides a foundation for the engineering applications of fractional-order hyperchaotic systems.

In this paper, the Adomian decomposition method (ADM) semi-analytical solution algorithm is applied to solve a fractional-order entanglement symmetrical chaotic system. The dynamics of the system are analyzed by the Lyapunov exponent spectrum, bifurcation diagrams, poincaré diagrams, and chaos diagrams. The results show that the systems have rich dynamics. Meanwhile, sliding mode synchronizations of fractional-order chaotic systems are investigated theoretically and numerically. The results show the effectiveness of the proposed method and potential application value of fractional-order systems.

Aiming at an entangled-chaos system with a memristor, the dynamic analysis and circuit realization are studied. Combining with the quadratic flux-controlled memristor, a memristive chaotic system is constructed, and the multistable behavior of the system when the initial value of the system changes is studied by using the system phase diagram, bifurcation diagram, and Lyapunov exponent spectrum (LE). Spectral entropy (SE), C0, and SampEn are used to describe the complexity of the memristive entanglement-chaos system. Finally, the multistable behavior of the system is realized by the digital circuit chip STM32. The experimental results are consistent with the system analysis and the numerical simulation of the MATLAB software. The experimental results of the circuit provide a foundation for the engineering application of the system.

A new 3D offset-boostable symmetric system is proposed by an absolute value function introduced. The system seems to be more fragile and easier to the state of broken symmetry. Coexisting symmetric pairs of attractors get closer and closer, and finally get emerged together. Basins of attraction show how these coexisting attractors are arranged in phase space. All these coexisting attractors can be easily offset boosted in phase space by a single constant when the initial condition is revised accordingly. PSpice simulations prove all the phenomena.