Neural Networks
What Are Neural Networks?
Neural networks are models that process information using interconnected nodes. These nodes, called artificial neurons, adjust weights and biases as they learn from data. Their layered structure helps them perform tasks like classification, prediction, and anomaly detection. Biological neural networks inspired their design.
Analyze Your Use Case
NYRIS employs neural networks to analyze thousands of image features in real time, enabling sub-second visual search across industrial catalogs and synthetic data generation from CAD models. This technology underpins solutions for spare parts identification, inventory management, and augmented reality integrations.
How Neural Networks Work
Neural networks function in three key stages:
- Input Processing: Raw data enters through input nodes, which assign numerical weights based on feature importance. For visual search, NYRIS’s networks analyze pixel arrays, detecting geometric and textural patterns in product images.
- Hidden Layer Transformations: Hidden layers apply activation functions like ReLU or sigmoid to weighted inputs, refining feature recognition. In manufacturing, these layers compare sensor data with training datasets to identify micro-defects in machinery.
- Output Generation: The final layer produces results, such as product matches or maintenance alerts. NYRIS’s networks process 500 million searches in under 0.5 seconds, leveraging GPU-accelerated parallel processing.
Industrial Applications
Manufacturing
Neural networks cut machine downtime by 73% using predictive maintenance. These systems analyze vibration and thermal data to detect faults early. NYRIS collaborates with DMG Mori and Trumpf, applying these models to CNC machines and laser cutters.
E-Commerce
NYRIS-powered visual search, integrated with SAP’s Business AI, improves product discovery in IKEA’s online catalog. Shoppable AR interfaces boost conversion rates by 18%.
Automotive
Renault trains autonomous vehicle perception systems with NYRIS’s synthetic data pipelines. Photorealistic CAD-generated environments lower LiDAR calibration costs by 40%.
Benefits for Your Business
- Operational Efficiency: Automate quality inspections with 99.7% accuracy, reducing manual labor costs by 85% in production lines.
- Scalability: Process 500 million product SKUs in under 0.5 seconds with NYRIS’s distributed neural architectures.
- Risk Mitigation: Predictive maintenance models cut unplanned downtime by 61%, saving manufacturers €2.4M per facility each year.
FAQs
How do neural networks learn without explicit programming?
Neural networks use backpropagation to adjust weights. They minimize errors by calculating gradients between predictions and ground truth. NYRIS trains models on synthetic CAD data, enabling component recognition without manual labeling.
Can neural networks operate with limited training data?
Yes. Transfer learning helps adapt pre-trained models to new tasks. With just 100 annotated images, NYRIS accelerates deployment in niche manufacturing applications.
About NYRIS
Founded in 2015, NYRIS pioneers visual search and AI-driven industrial solutions. Headquartered in Berlin and Düsseldorf, the company has secured €10 million in funding from Trumpf Venture and the EIC. Collaborating with SAP, IKEA, and Daimler, NYRIS delivers sub-second search engines and synthetic data tools. Its neural network frameworks cut supply chain delays by 34% across partner networks.