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Related Concept Videos

Trial and Error and Algorithm01:12

Trial and Error and Algorithm

A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light bulb,...
Associative Learning01:27

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Correlation of Experimental Data01:23

Correlation of Experimental Data

Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity, and...
Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Correlation and Regression00:53

Correlation and Regression

In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a negative...
Cognitive Learning01:21

Cognitive Learning

Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
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Related Experiment Video

Updated: Jul 7, 2026

Testing for Metacognitive Responding Using an Odor-based Delayed Match-to-Sample Test in Rats
08:06

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Published on: June 18, 2018

Trial-and-error correlation learning.

O Fujita1

  • 1NTT LSI Lab., Kanagawa.

IEEE Transactions on Neural Networks
|January 1, 1993
PubMed
Summary

This study introduces a novel neural network learning architecture for hardware. It efficiently modifies synaptic weights, outperforming backpropagation in finding global minima by emphasizing error reduction.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Engineering

Background:

  • Traditional neural network training, like backpropagation (BP), often requires complex architectures and can get stuck in local minima.
  • Hardware implementation of neural networks faces challenges in efficient and effective weight modification for learning.

Purpose of the Study:

  • To propose a new learning architecture for hardware implementation of neural networks.
  • To develop a weight modification strategy that avoids the need for complex backward error propagation.
  • To enable neural networks to escape local minima and converge to global minima more effectively.

Main Methods:

  • Introducing a novel learning architecture where each synaptic weight is intentionally changed per trial.
  • Modifying weights based on the correlation between weight changes and total output error.

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  • Implementing a trial-and-error correlation mechanism that can emphasize error gain over loss.

    • Main Results:

    • The proposed learning method closely matches backpropagation (BP) performance with small weight changes, without requiring a backward network.
    • Larger weight changes allow the system to explore the weight space more freely, avoiding local minima.
    • Computer simulations demonstrate superior convergence to the global minimum compared to BP when the correlation emphasizes error reduction.

    Conclusions:

    • The new architecture offers an efficient alternative for hardware-based neural network learning.
    • This approach mitigates the local minima problem inherent in many gradient-based learning algorithms.
    • The method shows significant potential for improving the training of hardware-implemented neural networks, especially in complex optimization landscapes.