Improving the Learning Speed in Topological Maps of Patterns

1990 ◽  
pp. 813-816 ◽  
Author(s):  
Joaquim S. Rodrigues ◽  
Luis B. Almeida
Keyword(s):  
Topological Maps ◽  
Learning Speed

Bold birds learn faster: Exploration style and learning speed are related

2009 ◽  
Author(s):  
Lauren M. Guillette ◽  
Adam R. Reddon ◽  
Peter L. Hurd ◽  
Christopher B. Sturdy
Keyword(s):  
Learning Speed

scholarly journals GateRL: Automated Circuit Design Framework of CMOS Logic Gates Using Reinforcement Learning

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1032
Author(s):  
Hyoungsik Nam ◽  
Young In Kim ◽  
Jina Bae ◽  
Junhee Lee
Keyword(s):  
Reinforcement Learning ◽  
Circuit Design ◽  
Logic Gates ◽  
Connection Matrix ◽  
Design Framework ◽  
Network Layers ◽  
Learning Speed ◽  
Fully Connected ◽  
Automated Circuit Design ◽  
Circuit Configuration

This paper proposes a GateRL that is an automated circuit design framework of CMOS logic gates based on reinforcement learning. Because there are constraints in the connection of circuit elements, the action masking scheme is employed. It also reduces the size of the action space leading to the improvement on the learning speed. The GateRL consists of an agent for the action and an environment for state, mask, and reward. State and reward are generated from a connection matrix that describes the current circuit configuration, and the mask is obtained from a masking matrix based on constraints and current connection matrix. The action is given rise to by the deep Q-network of 4 fully connected network layers in the agent. In particular, separate replay buffers are devised for success transitions and failure transitions to expedite the training process. The proposed network is trained with 2 inputs, 1 output, 2 NMOS transistors, and 2 PMOS transistors to design all the target logic gates, such as buffer, inverter, AND, OR, NAND, and NOR. Consequently, the GateRL outputs one-transistor buffer, two-transistor inverter, two-transistor AND, two-transistor OR, three-transistor NAND, and three-transistor NOR. The operations of these resultant logics are verified by the SPICE simulation.

scholarly journals Learning Application of Multimedia-Based-Computer Network Using Computer Assisted Instruction Method

2018 ◽  
Vol 7 (2.13) ◽  
pp. 341 ◽  
Author(s):  
Janner Simarmata ◽  
Tonni Limbong ◽  
Efendi Napitupulu ◽  
S Sriadhi ◽  
A R S Tambunan ◽  
...  
Keyword(s):  
Computer Network ◽  
Computer Assisted Instruction ◽  
Computer Assisted ◽  
Effective Learning ◽  
Network Computer ◽  
Learning Speed ◽  
Conventional Teaching ◽  
Assisted Instruction ◽  
User Friendly ◽  
The Given

In conventional learning, teachers frequently face difficulties to deliver their materials due to limited time and practical materials in teaching network computer. Conventional teaching process, especially practical materials, has still not yet been optimal. Thus, computer and multimedia based learning is required to help the students. Besides it can reduce costs in practical materials procurement, the students can absorb the given knowledge well without thinking of the costs to buy the practical materials. Computer Assisted Instruction Method can present the learning using various media either by picture or video that can assist the effective learning process and simplify the students to manage the learning speed since it is combined with the multimedia. By doing this, the students can practice the lesson materials, study whenever and wherever they want. Compuer learning application prioritize user interface, user friendly, which can make the students be diligent and passionate in learning. 

Forgetting Numbers in Old Age: Strategy and Learning Speed Matter

Gerontology ◽  
2005 ◽  
Vol 51 (4) ◽  
pp. 277-284 ◽  
Author(s):  
Anna Derwinger ◽  
Anna Stigsdotter Neely ◽  
Stuart MacDonald ◽  
Lars Bäckman
Keyword(s):  
Old Age ◽  
Learning Speed

Error Analysis of Neural Network with Rational Spline Weight Function

2014 ◽  
Vol 644-650 ◽  
pp. 2407-2410
Author(s):  
Dai Yuan Zhang ◽  
Jia Kai Wang
Keyword(s):  
Neural Network ◽  
Weight Function ◽  
Topological Structure ◽  
High Accuracy ◽  
Local Minima ◽  
Slow Convergence ◽  
Rational Spline ◽  
Fast Learning ◽  
Learning Speed ◽  
Training Neural Network

Training neural network by spline weight function (SWF) has overcomed many defects of traditional neural networks (such as local minima, slow convergence and so on). It becomes more important because of its simply topological structure, fast learning speed and high accuracy. To generalize the SWF algorithm, this paper introduces a kind of rational spline weight function neural network and analyzes the performance of approximation for this neural network.

Designing an efficient predictor model using PSNN and crow search based optimization technique for gold price prediction

2021 ◽  
pp. 1-9
Author(s):  
Rajashree Dash ◽  
Anuradha Routray ◽  
Rasmita Dash ◽  
Rasmita Rautray
Keyword(s):  
Mean Square Error ◽  
Optimization Technique ◽  
Absolute Error ◽  
Time Frame ◽  
Gold Price ◽  
Future Price ◽  
Mean Square ◽  
Learning Speed ◽  
Predictor Model ◽  
Hidden Layer

Predicting future price of Gold has always been an intriguing field of investigation for researchers as well as investors who desire to invest in present and gain profit in the future. Since ancient time, Gold is being arbitrated as a leading asset in monetary business. As the worth of gold changes within confined boundaries, reducing the effect of inflation, so it is a beneficial property favoured by many stakeholders. Hence, there is always an urge of a more authenticate model for forecasting the gold price based upon the changes in it in a previous time frame. This study focuses on designing an efficient predictor model using a Pi-Sigma Neural Network (PSNN) for forecasting future gold. The underlying motivation of using PSNN is its quick learning and easy implementation compared to other neural networks. The fixed unit weights used in between hidden and output layer of PSNN helps it in achieving faster learning speed compared to other similar types of networks. But estimating the unknown weights used in between the input and hidden layer is still a major challenge in its design phase. As final outcome of the network is highly influenced by its weight, so a novel Crow Search based nature inspired optimization algorithm (CSA) is proposed to estimate these adjustable weights of the network. The proposed model is also compared with Particle Swarm Optimization (PSO) and Differential Evolution (DE) based learning of PSNN. The model is validated over two historical datasets such as Gold/INR and Gold/AED by considering three statistical errors such as Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Empirical observations clearly show that, the developed CSA-PSNN predictor model is providing better prediction results compared to PSO-PSNN and DE-PSNN model.

Learning speed equalization network for few-shot learning

2022 ◽  
Vol 31 (01) ◽  
Author(s):  
Cailing Wang ◽  
Chen Zhong ◽  
GuoPing Jiang
Keyword(s):  
Learning Speed

scholarly journals Keyword Search over Probabilistic XML Documents Based on Node Classification

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Yue Zhao ◽  
Ye Yuan ◽  
Guoren Wang
Keyword(s):  
Keyword Search ◽  
Possible World ◽  
Xml Data ◽  
Fast Learning ◽  
Probabilistic Xml ◽  
Learning Speed ◽  
Xml Document ◽  
Probability Threshold ◽  
Node Classification ◽  
Learning Machine

This paper describes a keyword search measure on probabilistic XML data based on ELM (extreme learning machine). We use this method to carry out keyword search on probabilistic XML data. A probabilistic XML document differs from a traditional XML document to realize keyword search in the consideration of possible world semantics. A probabilistic XML document can be seen as a set of nodes consisting of ordinary nodes and distributional nodes. ELM has good performance in text classification applications. As the typical semistructured data; the label of XML data possesses the function of definition itself. Label and context of the node can be seen as the text data of this node. ELM offers significant advantages such as fast learning speed, ease of implementation, and effective node classification. Set intersection can compute SLCA quickly in the node sets which is classified by using ELM. In this paper, we adopt ELM to classify nodes and compute probability. We propose two algorithms that are based on ELM and probability threshold to improve the overall performance. The experimental results verify the benefits of our methods according to various evaluation metrics.

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