“ADAPTIVE NEURAL NETWORK FOR REAL-TIME DATA PROCESSING”

Abstract

ABSTRACT 5 The present invention relates to a novel, eco-friendly phytochemical-based formulation designed for effective mosquito larvicidal activity. The formulation is intended to target mosquito larvae (wrigglers), promoting larval death through natural mechanisms of lysis while minimizing environmental impact, chemical 10 resistance, and ensuring broad social benefits through public health improvements

Specification

Description:FIELD OF THE INVENTION
The present invention relates to the 5 field of artificial intelligence and machine
learning. Specifically, it pertains to an adaptive neural network model that adjusts
its architecture, layers, and learning parameters dynamically in response to realtime
data inputs, thereby enhancing processing speed, accuracy, and efficiency for
a wide range of AI tasks.
10
BACKGROUND OF THE INVENTION
Conventional neural networks are typically pre-structured with fixed architectures
and learning parameters. While these networks can perform well on a variety of
tasks, their performance can degrade when faced with real-time data inputs or
15 dynamically changing environments. In such contexts, static networks may struggle
to maintain optimal performance or processing efficiency due to the need for
manual reconfiguration, retraining, or hyperparameter tuning.
Adaptive systems, which can modify their own structure and learning parameters
20 in response to changing conditions, have shown promise in improving the
robustness of machine learning models. However, there is a lack of solutions that
allow neural networks to seamlessly adjust their layers, units, and learning
parameters specifically in real-time, as data is received.
25 There remains a need for a neural network that can automatically adapt its
architecture and learning parameters in real-time to optimize performance for
specific tasks while maintaining computational efficiency.
SUMMARY OF THE INVENTION
30 The invention disclosed herein is an adaptive neural network system designed for
real-time data processing. The system dynamically adjusts its internal structure-
3
including the number of layers, the number of neurons in each layer, and key
learning parameters such as learning rate, activation functions, and optimization
techniques based on incoming data and performance feedback.
The neural network is equipped 5 with an adaptive control mechanism that
continuously monitors the characteristics of the data being processed (e.g., data
complexity, input feature distribution, noise levels) and makes real-time
modifications to the network architecture. This enables the system to maintain or
improve processing speed and accuracy across a range of AI tasks, including but
10 not limited to classification, regression, clustering, reinforcement learning, and
time-series prediction.
DETAILED DESCRIPTION OF THE INVENTION
The following is a detailed description of embodiments of the present disclosure.
15 The embodiments are in such detail as to clearly communicate the disclosure.
However, the amount of detail offered is not intended to limit the anticipated
variations of embodiments; on the contrary, the intention is to cover all
modifications, equivalents, and alternatives falling within the spirit and scope of the
present disclosure as defined by the appended claims.
20 Unless the context requires otherwise, throughout the specification which follow,
the word "comprise" and variations thereof, such as, "comprises" and "comprising"
are to be construed in an open, inclusive sense that is as "including, but not limited
to."
Reference throughout this specification to "one embodiment" or "an embodiment"
25 means that a particular feature, structure or characteristic described in connection
with the embodiment is included in at least one embodiment. Thus, the appearances
of the phrases "in one embodiment" or "in an embodiment" in various places
throughout this specification are not necessarily all referring to the same
4
embodiment. Furthermore, the particular features, structures, or characteristics may
be combined in any suitable manner in one or more embodiments.
The headings and abstract of the invention provided herein are for convenience only
and do not interpret the scope or meaning of the embodiments.
The present invention relates to the 5 field of artificial intelligence and machine
learning. Specifically, it pertains to an adaptive neural network model that adjusts
its architecture, layers, and learning parameters dynamically in response to realtime
data inputs, thereby enhancing processing speed, accuracy, and efficiency for
a wide range of AI tasks.
10
The invention disclosed herein is an adaptive neural network system designed for
real-time data processing. The system dynamically adjusts its internal structure-
including the number of layers, the number of neurons in each layer, and key
learning parameters such as learning rate, activation functions, and optimization
15 techniques based on incoming data and performance feedback.
The neural network is equipped with an adaptive control mechanism that
continuously monitors the characteristics of the data being processed (e.g., data
complexity, input feature distribution, noise levels) and makes real-time
20 modifications to the network architecture. This enables the system to maintain or
improve processing speed and accuracy across a range of AI tasks, including but
not limited to classification, regression, clustering, reinforcement learning, and
time-series prediction.
1. Adaptive Network Architecture
25 A set of input layers that receive real-time data inputs.
A series of hidden layers, where each layer's configuration (i.e., number of neurons
and connections) can be modified based on real-time analysis of the data.
5
Output layers that produce predictions or classifications based on the processed
data.
2. Real-Time Performance Evaluation Module
A key feature of the invention is the inclusion of a real-time performance evaluation
module that tracks the accuracy, loss, and 5 computational efficiency of the model.
This module analyzes the data being processed in real time, monitoring variables
such as:
Input data complexity (e.g., dimensionality, distribution).
Output performance metrics (e.g., prediction accuracy, error rate).
10 System utilization (e.g., computational load, memory usage).
The evaluation module generates performance feedback, which informs the
adaptive mechanism about the required changes in network architecture or learning
parameters.
3. Adaptive Control Mechanism
15 Layer Adjustment: Dynamically adding or removing layers based on input data
characteristics, task complexity, and model performance.
Neuron Adjustment: Modifying the number of neurons in each layer to enhance
model expressiveness or reduce overfitting.
Learning Rate Optimization: Automatically tuning the learning rate or selecting the
20 most appropriate optimization algorithm (e.g., SGD, Adam) based on data
properties and training progress.
Activation Function Selection: Switching between activation functions (e.g., ReLU,
Sigmoid, Tanh) for different layers depending on the task and data type.
6
4. Data Stream Integration
Incremental updates to the model's weights and parameters as new data arrives.
Incorporating temporal dependencies in the data (e.g., for time-series analysis) to
adjust the model in response to trends, seasonality, or noise in the data.
5 5. Adaptation Algorithms
Gradient-based Adaptation: Modifying network architecture and hyperparameters
by gradient descent or variants thereof.
Evolutionary Algorithms: Utilizing genetic algorithms or other evolutionary
methods to explore and select optimal network configurations.
10 Bayesian Optimization: Applying Bayesian techniques to determine the best
hyperparameter set in real time.
6. System Benefits
Improved Accuracy: The ability to adapt the network to changing data leads to
higher accuracy, particularly in non-stationary or uncertain environments.
15 Faster Processing: By optimizing the network's complexity based on data demands,
the system reduces unnecessary computations, leading to faster processing.
Resource Efficiency: The adaptive nature of the network ensures that computational
resources (e.g., memory, CPU/GPU) are utilized efficiently, improving scalability
for large datasets and real-time applications.
20 Seamless Operation: The system operates autonomously, without requiring manual
intervention to tune parameters or adjust architecture.
EXAMPLES OF APPLICATIONS:
7
The adaptive neural network can be applied in various domains, including but not
limited to:
 Real-time Video Processing: Adapting the model to process varying
resolutions and scene complexities in video streams.
 Financial Forecasting: Adjusting 5 the model's complexity as new financial
data arrives, optimizing for accuracy in predictions.
 Autonomous Vehicles: Dynamically modifying the network to account for
changes in environmental conditions, traffic patterns, or sensor inputs.
 Healthcare: Real-time adaptation for processing medical data streams,
10 optimizing for diagnostic tasks with varying levels of data noise and quality. , Claims:1. A method for adaptive real-time data processing using a neural network,
comprising the steps of:
a. Receiving a real-time data stream;
b. Analyzing the data to assess it 5 s complexity and characteristics;
c. Adjusting the number of layers and/or neurons in the network based on
the analysis;
d. Modifying at least one learning parameter, selected from the group
consisting of learning rate, activation function, and optimization
10 algorithm, in response to real-time performance feedback;
e. Outputting predictions based on the processed data.
2. The method of as claimed in claim 1, wherein the step of analyzing the data
includes evaluating the complexity of the data, including dimensionality and
distribution.
15 3. The method of as claimed in claim 1, further comprising the step of adjusting
the network's architecture incrementally based on a performance evaluation
feedback loop.
4. An adaptive neural network system for real-time data processing, comprising:
a. An input layer configured to receive real-time data inputs;
20 b. A dynamic set of hidden layers capable of adjusting their configuration
based on data complexity;
c. A performance evaluation module configured to monitor processing
accuracy and efficiency;
d. An adaptive control mechanism that modifies network layers, neurons,
25 and learning parameters in response to real-time performance feedback.
5. The method of as claimed in claim 1, wherein the adaptive control mechanism
utilizes gradient-based adaptation, evolutionary algorithms, or Bayesian
optimization to modify the neural network's architecture and parameters.

Documents