Recursive Neural Network-Based Market Demand Forecasting Algorithm for Calligraphy Practice Products

In today’s society, calligraphy, which reflects one’s basic writing skills, is becoming more and more important to people. People are influenced by calligraphy in their studies, work, etc. Improving calligraphy writing skills has become one of the key directions for developing one’s abilities at this stage. As an important means of improving writing skills, calligraphy practice products are attracting more and more attention and purchases. In particular, in recent years, as the market economy has developed in a deeper direction, people’s demand for calligraphy practice products has diversified and calligraphy practice product companies have launched a variety of products to meet the public’s calligraphy practice needs in order to adapt to the reality of consumer demand. However, with the development of the Internet culture industry and influenced by objective factors such as school holidays and seasons, the current market demand for calligraphy practice products is rapidly and dynamically changing, making market changes difficult to grasp and leading to poor sales, which directly affects the profits of calligraphy practice product-related companies. The artificial intelligence neural network method realizes the nonlinear relationship between the input and output of sample data through the self-learning ability of each neuron and has a certain nonlinear mapping ability in prediction, which plays a great role in the market demand prediction of many commercial products. Based on this, this paper proposes a recursive neural network-based algorithm to predict the future demand and development trend of calligraphy practice products through extensive and in-depth research, so as to provide positive and beneficial guidance for enterprises’ future production and sales.

RECURSIVE NEURAL NETWORK MODEL OF CATAMARAN MANOEUVRING

A neural network model to simulate catamaran manoeuvres is proposed as an alternative to the traditional methodology of developing manoeuvring mathematical models. Data obtained in full-scale trials with a real ship are used to train the model. By recording full-scale trials of catamaran manoeuvres it is possible to generate a neural network model which will allow the prediction of the catamaran manoeuvring performance under different conditions. A Recursive Neural Network (RNN) manoeuvring simulation model is proposed and applied to a catamaran in this specific case. Inputs to the simulation are the orders of rudder angle and ship’s speed and also the recursive outputs velocities of sway and yaw. Two types of manoeuvres are simulated: tactical circles and zigzags. The results between the full-scale data and the simulations are compared in order to analyze and determine the accuracy of the RNN. The study is performed for a catamaran operating in the Tagus estuary for passenger transport to and from Lisbon.

Multi-objective QPSO for short-term load forecast based on diagonal recursive neural network

The short-term load forecast is an important part of power system operation, which is usually a nonlinear problem. The processing of load forecast data and the selection of forecasting methods are particularly important. In order to get accurate and effective prediction for power system load, this article proposes a hybrid multi-objective quantum particle swarm optimization (QPSO) algorithm for short-term load forecast of power system based on diagonal recursive neural network. Firstly, a multi-objective mathematical model for short-term load forecast is proposed. Secondly, the discrete particle swarm optimization (PSO) algorithm is used to select the characteristics of load data and screen out the appropriate data. Finally, the hybrid multi-objective QPSO algorithm is used to train diagonal recursive neural network. The experimental results show that the hybrid multi-objective QPSO for short-term load forecast based on diagonal recursive neural network is effective.

Improving ENIGMA-style Clause Selection while Learning From History

We re-examine the topic of machine-learned clause selection guidance in saturation-based theorem provers. The central idea, recently popularized by the ENIGMA system, is to learn a classifier for recognizing clauses that appeared in previously discovered proofs. In subsequent runs, clauses classified positively are prioritized for selection. We propose several improvements to this approach and experimentally confirm their viability. For the demonstration, we use a recursive neural network to classify clauses based on their derivation history and the presence or absence of automatically supplied theory axioms therein. The automatic theorem prover Vampire guided by the network achieves a 41 % improvement on a relevant subset of SMT-LIB in a real time evaluation.